You're listening to a stage talk titled Monitoring
the Environment from Space. This week we were
joined by the technical director from Space for
Good as they walked us through the possible practical
applications for satellite imagery when it comes
to environmental monitoring, explaining how and
why satellite images may be the best way to oversee
large -scale rapid impacts on our climate and
wildlife. This talk was hosted by my colleague
Giancarlo Fiorella on Thursday the 10th of April,
2025 in the Bellingcat Discord server. Welcome
everybody. My name is Giancarlo Fiorella and
I'm the Director of Research and Training here
at Bellingcat. I am filling in for Charlie this
week. who is away at a conference. I'm really
excited to introduce you to Lisa Bruckhausen.
Lisa is the tech lead at Space for Good and is
a remote sensing specialist. Lisa and her team
use satellite imagery and remote sensing techniques
to monitor changes in the environment from space.
Now, previously we'd spoken about a tool from
a Bellingcat contributor named Chris Giles. And
the name of the tool, you might remember, was
the OSINT Forest Area Tracker. This is a tool
that looks at burned areas of forest in Ukraine.
And the purpose of the tool is to give users
the ability to look further afield about forest
fires in this part of the world. Now, that tool
uses something called Sentinel -2 data. You can
find it on our GitHub. If you go to GitHub and
you look for Bellingcat, this is one of the many
wonderful tools you'll find there. Space for
Good have just come out with their own application
to help people on the ground monitor deforestation
from space. And Lisa is here to talk about that
application and more of her work at Space for
Good. Now, if you'd like to ask a question, please
do so in the chat as we talk. I'll be writing
down your questions and then asking them to Lisa
towards the end of the talk. If you don't want
me to read your username when I ask your question
to Lisa, please make a note of that in the chat.
Lisa, thank you so much for being here this afternoon
in the Netherlands. I'm going to hand over the
microphone to you now. Thanks very much for having
me. Thanks everyone for being here, for listening
in and a special thanks for Giancarlo and Charlie
for setting this up and of course everyone here
for all the good work. So I'm here today to share
some of the work that we are doing and share
some ideas and some inspiration on hopefully
open some eyes on how a lot more can be done
with the eyes we have in the sky, the satellites.
I'm Lisa. I'm technical director of Space for
Good and today I will focus mostly on the practical
use cases with satellites. So I'll talk a bit
about that, like I do every day and hopefully
after this so will all of you. At Space for Good
we work with satellite data to monitor the earth.
We do this for projects we believe have a positive
impact on the planet. I'll talk through some
of the work we've done, some big developments
and some smaller ones which nevertheless I think
have a good impact. and hopefully share some
ideas and inspirations on what's possible out
there. During this session I will not get technical,
but rather provide with a satellite overview.
However, you're more than welcome to ask technical
questions at the end, hoping for those. And when
I say monitor the planet with satellites, I don't
just mean working with pictures from space. I
mean uncovering the stories. Environmental stories,
land use stories, sometimes very personal stories.
where the start of the story starts with the
data. So first of all I didn't want to talk about
Forester, our product to help detect illegal
logging near real time in the field. It's a tool
to give rapid alerts and really help Foresters
on the ground take action rapidly rather than
having long -term visuals over trends or regional
actions. We rolled this out first of all in Indonesia.
We're working with local partners to take direct
action. Sometimes we use Sentinel -2 and need
the optical data. For this, we use Sentinel -1,
which is a radar data set, which means we can
penetrate the clouds. Now, if we look at tropical
rainforest, then rain kind of in there. There's
a lot of precipitation, a lot of cloud cover.
That means that with optical data, we often have
just see the clouds. With radar data, we can
look through the clouds and see changes that
are happening. It's a bit less intuitive to read
the data, but that's what we have algorithms
for. So we look for changes, rapid changes, within
areas that we before classified as forest. Within
those areas, we look whether it's likely illegal
logging or a legal or natural event such as a
landslide. If we think it's illegal logging,
we send the WhatsApp alert out to people in the
fields directly. They go and they take action.
So what we've seen before is that they come up
with security guards to show the people that
they have been spotted and to get the people
out. Sometimes they use the reports we generate
to go to local authorities and get the police
involved. And with that, they can remove the
people before a big conversion to agriculture
happens, which means that the forest can recover
faster and better. We now have a system that's
over 98 % accurate, so that's really nice. In
this area at least. In other areas we always
need to retrain and revalidate because it's not
a globally applicable model, but it's a model
that really needs to be trained locally because
the patterns and the nature is different in different
locations. A bit about the stories. So one of
the things we hear from our clients is that they
can finally detect deforestation that happens
away from roads. Imagine here it's a tropical
rainforest condition. It's about 200 ,000 hectares
of area to monitor. Roads tend to be washed away
with rainfall or any other events and suddenly
the road is gone. And even if the roads are perfect,
yeah, it's a rainforest. You can't look into
the forest and see very far. So you can only
see a few meters. So before it was almost impossible
to see these events because they happen outside
of the direct road network, outside of direct
fuel. And one more thing that we've seen, which
I really like talking about. I don't like the
story behind it. But we detect a full satellite
view of the whole area, which means that we detect
all deforestation events. What they noticed is
with that data that around one security guard
was positioned at different security posts. Whenever
he was positioned, there would be a peak in illegal
logging. So likely he let some family members
through illegally but that's very difficult to
assess if you don't have the full data. So by
having that full map it could pinpoint hey here
we need to do some more research to identify
what's exactly going on. Quite a nice case to
see like how this information we never thought
something to be pinned down to an individual
but that actually was possible with the help
of the data and of course merging it with all
sorts of other data sets. Now what we do here
differs quite a bit from EUDR compliance because
we are focused on near real -time direct action
and I am assuming directly that almost everyone
knows what EUDR is but some of you might not
hear about it as much as I do so let me quickly
expand EUDR is the European regulation for deforestation
free supply chain starting next year probably
you never know with EU it's intended to ensure
that agricultural products entering the EU are
not causing deforestation so that you can eat
your bar of chocolate guilt free. Deforestation
wise I'm not talking about fair pricing or human
rights or calories today, but that it doesn't
cause deforestation. EODR has a single definition
of forest deforestation land cover where we rather
look at trees disappearing. So we don't look
at is a plot of forest converted to agriculture,
but we look at there were trees here, there are
none now, take action. rather than saying there
were trees here, there's agriculture, two years
later, so you can't import. And then we really
focus on enabling people in the field to take
direct action. That's a bit about forest application,
but deforestation, but it's not all bad. There's
also afforestation, the growth of new forest,
where new forests are being planted. So, nice
story here. without naming specific names I'd
like to come into a little case study we did
where we got a request from a small tree planting
NGO to compare how some of their different sites
have been doing they used to do a lot of field
work but with environmental more and more information
about the flying but also safety concerns for
quite a long period they couldn't go into their
afforestation areas anymore so we did some analytics
for them to compare how different sites were
doing, which one was healthy, which one was managed
well etc etc and in some sites we found that
there were no trees left so the local partners
had been receiving money to take care of the
trees but had failed to report the death of several
full plots so we're gonna go by mistakes happen
but thanks to this data the project could be
changed around gain additional management and
it was made clear to the people locally that
there were checkups being done which is, first
is good, but validation is better. And of course
it's bad that those trees didn't make it, but
with satellite data, they could make early interventions
and changes into the process. Now in this project,
we also made some nice analytics, some quite
nice algorithms we developed to compare the different
sites, the missing trees. I would like to say
that there was this amazing algorithm in -depth
assessment. But basically it was looking at a
picture and going to a colleague and, do you
see trees? I don't see trees. And having figured
out that there was a major issue here. Satellite
data analytics can be rocket science, but often
it's not. And then having that layer of extra
accountability, especially in an industry where
so much greenwashing is happening, can give that
additional trust. And not having that layer...
and lead to more scrutiny which at least leads
to us all having something to do so that's also
a win right? Looking at the current Belenka challenges
there's also I also want to quickly point out
some other things everything that has a location
always grab a satellite image we did a project
around illegal zoos small project there was no
budget available so we had no commercial data
but even looking at 10 meter resolution openly
available satellite data It's the Sentinel -2
program that's looking at only true color images.
We could see the expansion of some of these wildlife
breeding facilities as an extra source of information
to help with the investigation of that party
there. And animals or meat directly leads to
biodiversity. We see a spike in biodiversity
credits. where people are paying to support biodiversity
within different areas, within different conservation
sites or new nature developments. What is missing
in a lot of cases is objective data. There is
quite a lot we can see with satellites that relates
to biodiversity. Very rarely you look at individual
species, but often you get landscape features,
diversity, landscape history, all those things
can be mapped. That also brings us to an issue.
If we can map multiple things, we can get different
outcomes. And which outcome do you share when
all of the different outcomes are correct, but
also saying something different? I don't always
have an answer to that, but I do know that the
answer can't always be the most optimistic case
or the most profitable case. Even if we don't
look at the complexities of biodiversities, but
look into carbon credits, which is something
measurable theoretically, you can measure a tree.
We see billions flowing into the carbon credit
market, but are the protected areas even under
threat? And that's why satellites are very important
to generate a baseline to see what has been happening,
because we can look back until the 70s of the
last century to see evidence -based what has
been going on. We can provide support whether
a project is resulting in avoided deforestation
or an aforestation, and identify good reference
areas, or make strong claim why a reference area
chosen, so an area where we compare the prediction
site with chosen, is selected wrongly. And the
net satellites can give a timeline rather than
snapshots to tell the whole story instead of
part of the story. We see that a lot with land
cover conversion, where land is changing from
forest to crop land, from pasture to urban, and
we can detect whether that change is cyclic.
So if you have shifting cultivation, every year
part of a forest is cleared to convert to agriculture.
That's not always the same as a fool on deforestation
because maybe a year later a different area is
cleared and it's part of the system that's being
used within the region. But often it's not. Often
it's just a cascade effect. Like we see in Indonesia,
where we see in certain areas a pattern from
primary forest. to the graded forest, to small
water fields, to commercial palm oil. And that
whole pattern can happen within a few years.
Also, I want to quickly talk about invasive species
monitoring. We do quite a lot of work in Indonesia.
We now started just out the project to map invasive
grasslands to support suitable afforestation
areas. For us, that's pretty much the same as
looking at making a land cover map, saying what's
there. It's a bit more complex if we look at
very specific species or species group. Almost
everything you can see with the naked eye, especially
if you can look from above and see it. You can
also model and map on large scale with satellites,
but also develop triggers, codes that give a
trigger if something happens. That'd be quite
nice to give warnings. And then I think my current
favorite project to talk about is environmental
risks. Imagine you spend a lot of time and resources
to map a minefield in Ukraine. Imagine a dam
breach or storm causing a flood. Mines get shifted
with the water. They're relocated. You don't
know where. You need to remap possibly a much
larger area to figure out where the mines are
in order to be cleaning them. With that we're
working with the Halo Trust to map environmental
risk to support in mine clearance priority setting.
So where is a fire likely to happen? or a flood,
like a fire you can't exactly send a fire truck
into a minefield if there's a forest fire. Or
where is a drought likely to happen and what
communities will that affect? That's all information
that can be helped in this case for better decision
making but that's all information that can also
be generated globally for different purposes.
So really looking into the different risks based
on the patterns that we've seen and based on
the patterns that we expect in the future with
for example climatic differences. And if a fire
happens, where, how big, the faster the information
can reach the right people, the better the action
can be taken. So I can really recommend everyone
to, if you haven't already, to look at the NASA
Global Database firms. It provides information
on patterns and trends and historic fires. So
that way we can map and also make predictions.
We can give alerts, we can map recovery, always
globally and everywhere available. and objective.
I say objective, but in this time of deep fakes
and fake data, how can we be sure that satellite
data can be trusted? Or how can we know when
it can't? So far, large datasets have had very
few large -scale issues, and I should be careful
what I say with this audience, because the satellite
data can feel inherently objective. It's from
space. It looks like the truth. But as soon as
it hits the ground or website, it can be framed,
processed, and cherry -picked to give a particular
story or a particular angle. And we can't pretend
like the data is always going to be correct.
So how to make sure the data is not manipulated
or misrepresented and how to make sure that you
don't accidentally misrepresent the data. Quite
simply put, there's a lot of different satellites
out there, so don't rely on a single image. Look
at the time series, look at what change happened.
Is it a sudden or gradual change? Is it a seasonal
change? For example, if you look at the deforestation
events, you can make very impressive images about
European deforestation. by comparing summer and
winter images, because the trees lose their leaves
and everything looks dead. It's not. Guys, the
forest survives. So really look at how does this
thread, how is what we're seeing normally looking
over time? And how is it looking with multiple
sources? So there's a lot of different satellite
constellations out there, both public and commercial.
Satellites are a witness. and you don't rely
on one witness when you've got a dozen out there.
We have, for example, very nicely Sentinel and
Landsat, where Landsat is hosted by the NASA.
Sentinel is from the European Space Agency. So
different sources that can look at the same thing,
different sensors, different resolutions, et
cetera, et cetera, but they can see the same
trends. So always have a look at, hey, can I
validate this with a different data set? And
then if you're truly concerned about the validity
of the data, heading back to the original source.
So a lot of the data you can find in different
places has been processed, which is freaking
amazing because I can tell you atmospheric correction
is really annoying to do. And usually you don't
really need to do it yourself. You can quite
easily get that done automatically. But if you
have reason of doubt, it's good to check the
original data source and go back into that. and
to check the full data source. So we're all used
to look at true color images, red, green, blue,
make a very nice picture. People working a lot
with satellite data tend to work a lot with the
near infrared band as well, make false color
composites. Looks really nice. You can see a
lot more changes. But it's a lot harder to fake
23 bands of sentinel imagery, including atmospheric
effects, than it is to fake a single picture.
You'll look back to that single image. that original
data set and see, can you validate the whole
processing chain or not? And with false position,
I think that's more of a misrepresentation usually
than a true hacking into the data. If people
make strong claims about data, then the models,
then the evidence should back that up. If they
make a... If they don't share any uncertainty
estimates, that's not that relevant. I now share
the accuracy of our model in Eurasia and I can
tell you that's tested on one side and it's amazing
there. But I can also tell you that I can make
a global deforestation detection algorithm that's
99 % accurate. I would do that. I would say that
in the sea, there will never be deforestation.
So that's already 60 % done. I will say that
outside the forest, there will never be deforestation.
So then we only have a small area to check. And
actually saying there's less than 1%, there's
only a couple of percent of deforestation. That
means that if I say there's zero deforestation
globally, I have a global model that's going
to be 99 % accurate. It's also going to be absolutely
useless, but it's going to be accurate. So know
when people stroll around accuracy metrics, what
to ask for. I can share the accuracy metrics
about our model as well if you want, by the way.
Those are reliable. And ask those right questions.
Or make sure you or someone you can depend on
can do the actual analysis. And know that the
data can be hacked. Processing pipelines can
be tampered with. Cloud platforms can be compromised.
Scripts can be manipulated. very easily putting
in an altered timestamp, we've all seen that
happening, is already giving a completely different
view of what the data represents. So that's why
open codes, audit trails, their reviewed methods
are important. So satellite data is a powerful
witness, but it does need cross -examination.
It can be mayplated, it can be wrong, but it
is objective. And with the right checks, it can
also be a very strong ally in the truth. So to
end a bit cheesy, the eyes in the sky are watching,
but they're on our side if we know where to look.
Where this forest lost ghost plantation or land
grabbing satellite data can be an ally. So start
exploring, ask your questions now or contact
me later for more information. If you can't find
me based on what I've just said then you shouldn't
be in this group. So thank you all for listening.
Thanks very much, Lisa, for your presentation,
for sharing your knowledge and your experience
with us. Folks, if you've got questions for Lisa
based on what she just said, please type them
in the chat. I'm watching the chat. I will copy
your question and ask it to Lisa later on. Lisa,
I've got a couple of questions here for you just
right off the bat as people get warmed up here
with their own questions. Can you tell us a bit
about what got you personally interested in satellite
data? What was your journey like into this field
from maybe a young person in the university or
high school to where you are today? How did you
get interested in this and how did you end up
working at Space for Good? Yeah, thanks. It's
been a while since I thought about that. In high
school I wanted to really go into nature conservation,
going into biology, but then I saw that biology
was going to be very much looking into cells
in most universities and looking into the cell
processes and that was not what drew me. I really
wanted to look into ecology and the bigger processes
and see how those things work. So then I started
to study forest and nature conservation in Wageningen.
I did my bachelor's there. I started my master
and then I got a bit into GIS remote sensing
and spatial modeling. And that was for me really
what I was looking for. It's working with the
computer, making some models, figuring out how
things actually work and can we replicate that.
Basically making major conservation into a game
and seeing like, how can I alter that? And that's
really drew me and then... slowly but surely
I started to learn more about these satellites
and everything I could see. And basically with
everything I did, I was like, okay, but where
can I see, what can I see that's going on? And
that really got me excited to every time. But
the data says, yeah, I'm kind of nerdy. Great.
Thanks for that answer. And as a kind of a follow
-up, if you're a person, maybe you're listening
to this live here in our Discord server, you're
listening to this later. and you are interested
in getting into this kind of work or you're hearing
for the first time, oh, wait a minute, there's
these satellite imaging platforms and one of
them is called Landsat and the other one is called
Sentinel. If you're just learning about this
now or you're becoming interested in this now,
what are some recommendations that you have for
somebody like that to learn more about this field
and to maybe get their hands on some satellite
imagery and start looking at them? Yeah, so the
very, very first step is Google Earth, where
you have the time view as well. So you can look
back in the past and you can start playing around
a bit and get hopefully excited. Then I would
recommend to work mostly with Sentinel, Sentinel
-2, which is optical data. It's a bit easy to
work with and radar data, for example. And it's
10 meter resolution, so you can see quite a lot
of different things. You can download it either
via one of the DS servers or Google Earth engine.
Google Earth Engine is still freely available
for certain use cases. So that's a nice win.
Unfortunately for a lot, not anymore, which is
too bad. But then Google Earth, you can, there's
a lot of tutorials and a lot of different learnings
out there to be looking at things. And yeah,
start with seeing if you can map your own house
and different bands in different views. Look
at any article you're reading like, okay, what
is the latest image I can get? Get really, really
frustrated by cloud cover and figure out how
to deal with it. And then at one point think,
okay, now I'm going to need radar data to complement
this or now I'm going to need Landsat data to
complement this. Have a look and play around
with it. It is coding based. You can also look
at QGIS, Quantum GIS, which is a software you
can install your computer and then just download
the data and open it in there. But I recommend
working coding based because you can all the
things so much more easily. Thanks for all those
tips there. By the way, I'm dropping the links
to everything that Lisa's mentioning here in
our Discord server chat. So if you take a look
at the chat there, I've dropped a link to Google
Earth Pro, which, yes, is maybe the most beloved
and used tool of the open source researcher,
whether you're working primarily with satellite
imagery or... You're doing geolocations for human
rights investigations. The Sentinel Google Earth
Engine, I've also dropped a link in the chat
there, and QGIS as well. If you're listening
to this later and you don't have access to the
chat, you just Google for those words that I
just mentioned, and that Lisa just mentioned,
and then you'll see links to these tools that
you can download. We have a question from Fraser,
community member Fraser. Hello Fraser, good to
see you. Thanks for being here. Fraser has this
question. Lisa, Fraser asks, have you had a look
at the Leiden guidelines on digitally derived
evidence and satellite imagery? Yes, I had a
look at it, but not recently enough to be answering
this in this. I do know there's a couple of different
works out there at the moment, also with CivicSpace
looking to launch their own satellite constellation
that is not from any... any government or any
agency or any commercial entity but it's really
looking at evidence -based blockchain validated
data. I've had a look at it and I definitely
will have a look at it again and I recommend
everyone to have a look at it and I will not
go further into that. Yeah, oh great, that's
an answer. Hey, that's a great answer. Thank
you for that answer. Thanks for your question,
Fraser. And by the way, if you have any questions,
please type them in the chat here. I will ask
them to Lisa. As we continue, we got about a
half an hour left, so do not be shy. Lisa's an
expert in this field. You don't often have a
chance to talk to somebody like Lisa, so do not
be shy. Ask your questions in the chat, please.
I got another question for you here, Lisa. The
question is this. Towards the end there, you
talked about deep fakes and whatever we want
to call AI -generated images and fake data being
out there. You mentioned... that there were,
I think you said, very few large -scale issues
out there with imagery. I know in our own work
at Bellingcat, we're often asked, can you think
of examples of AI images in the wild that you've
detected, that you've worked with? And certainly
in our field, we've seen quite a few. So my question
to you is, can you think of examples of AI -generated
satellite imagery? Are they out there? Have you
come across them? Are there people out there
who are trying to trick others into looking at
a satellite image and saying it shows something
but really it's generated by AI? Is that something
that you've come across? And if not, is that
something that you fear might happen soon? Yeah,
I have not come across any big ones I come came
across when doing some research for this to also
find some an area where someone made a nice fake
post about a fire in In a big park which was
like obviously fake as in it looked nice, but
it was also more made with Photoshop So then
there's no data behind it. I do think it's likely
to be happening in the future if not now already,
especially with high resolution data where there
is active tasking. So there's often only one
image or very few images to be altered basically.
And then if you look at war zones, for example,
there's very high stakes. So I can imagine it
would be naive to say that it's never going to
happen. It's likely to happen. But I think with,
especially in the case where you have open data,
we have time series of the same data sets, it's
easy enough to disprove. oh wait that's not I
think that are easy to disprove still get sometimes
picked up by the crowd but it is at least easy
to disprove so it is something to be conscious
of but that's not yeah for the deforestation
I don't see it as the direct biggest risk factor
yeah thanks for that and you know it's interesting
when you were going through the workflow or the
suggestions on how to work with images satellite
images in this time of fake images, of AI images
being out there. The steps that you outlined
are the same, many of them are the same as we
recommend to people who ask us about, hey, how
do I know that an image from a protest is really
a real image and not an image from, or how do
I know that this picture from a war zone really
is an actual picture from a war zone and not
a fake picture? You know, you talked about looking
for other images, right? So you called it the
time series, like look at other pictures that
apparently show the same thing. Because I think
I suspect that as with pictures from war zones
or from protests or from anywhere that are generated
by AI, the platforms can't, at least I'm not
aware of this, can't replicate the same scene
from different angles. But as you said, it's
kind of naive to think it's not going to happen.
I suppose for satellite imagery, something that'd
be interesting to think about is satellite images
are... They show intricate patterns right like
roads and houses and and and I wonder if the
AI image generation technology is so good that
it can actually accurately mimic those and I'm
thinking specifically about the cover of the
Berkeley protocol book from the Human Rights
Center at Berkeley. The cover of that book is
actually an AI -generated image of a satellite
view. But if you take a close look at it, and
an expert eye like yours could definitely tell,
hey, this is weird. I've looked at a million
satellite images. None of them look like this.
But as you say, it's one of those things you
have to think about as being potentially possible
in the next little while. There, indeed, my risk
is there is especially with open satellite data,
there is a lot of data available to train on.
And a couple of years ago, the generation of
deepfakes was very, very early on. And if you
see how fast that goes, how good video material
we can get. I'm not sure why I'm here. I could
have just sent an AI. How good that all gets.
Then I think, yeah, the satellite data, there
is a lot of data out there. is going to be feasible
as well. The patterns at the moment, I don't
think yet, but I'm talking to some people who
are working on how to connect the roads, which
is also very useful if you want to map a road
network in an area. It's nice to be able to map
those roads. But yeah, it can be used to generate
fakes as well. And if we talk about the wrong
data, I see at the moment a bigger issue in misrepresented
data. having a data set and saying it's and that's
something that we do see what people say claim
it's in a different area and again you can say
or a different time and you can say no wait i've
got the evidence that it's not but if the information
is out there coming with the evidence isn't always
enough yeah thanks for that lisa i have another
question here since we're talking about you know
we're talking about what may be coming in the
future in terms of ai image generation you've
just mentioned this ability to make realistic
roads, for example. Are there any advances in
remote sensing technology that you think are
coming soon or in the next couple of years that
you're really excited about? A lot, a lot. So
what's happening right now is Google has just
announced a better version of a system to be
talking to maps, basically. So rather than doing
all the investigation yourself, being able to
just ask, hey, for this area of interest, how
much deforestation has there been? I think there's
a big risk there, but I also think there's a
lot of opportunities there to take that first
step into how can we actually see what's going
on on our planet? I think the roads, that's for
me to make the system of automatically connecting
different features. based on a lot of data, but
also based on quite complex models. Having a
detailed road network can mean so, so much for,
for example, detecting illegal logging, but also
predicting it. We see that illegal logging almost
always happens near roads and almost always first
a new road is made and then logging happens and
it expands. If you can see that earlier by having
just a few points. then you can really map out
a lot so that's really interesting to me and
yeah biodiversity making some sort of integrated
solution how can we map biodiversity to well
it's not really clear what biodiversity there's
not one definition of biodiversity but to be
able to say hey this is going well and this is
not going well Thanks, Lisa. We have a question
from Chris. And if you have a question, folks,
please type it in the chat. We've got about 20
minutes left here with Lisa from Space for Good.
Chris is asking this question here, Lisa. Now,
there's some jargon in here that I don't know.
So I'm going to read it out as it is written.
And maybe this is the jargon that you know. And
the question is this. I'm curious to hear more
about how you are ground -truthing satellite
data to help detect distortions. So we ourselves
are not earlier, but let me first like phrases
a bit for the rest. So ground rooting that's
looking at what's on the ground and perceiving
that as the truth and you can use that to retrain
models. I think here the distortion, the distortions,
but Chris, chat if it's wrong, is really looking
at the distortions of the images, which is why
you're looking at, for example, something that
we've seen in the past quite a lot, the earlier
generations of planet data had a lot of misalignment
of different bands now i'm getting a bit technical
here so that's when you have a picture of the
red green and blue and they're all not exactly
aligned so you could have a situation where the
red value of a certain pixel was 10 meters below
blue value so you would have a shift there with
ground rooting you can basically align pictures
so they're all exactly at the same spot. And
then you can look for example, changes and then
you know where something is exactly. Usually
that is done by the satellite data providers.
And I think right now, most of those have good
enough on -train models that we can focus on
our own strengths, which is mostly the data analytics
and going into depth on the AI models. Thank
you for that question. And thanks for that answer,
Lisa. I have another question. What is your kind
of biggest frustration about the limitations
of the current technology? What's the thing that
you run across the most that makes you go, oh
my gosh, I wish it wasn't like this. I wish we
could fix this issue with the technology. What's
something that's missing in the technology or
a capability that's missing at the moment? Yeah,
it really depends on use case to use case. What
is, for me, I think my biggest frustration is
not as much in the technology, but in the technology
communication. So I'm spending a lot of time
with different users and different potential
users talking about, hey, what can be done and
what cannot be done. And then sometimes you have
people who say, who think we're working with
satellites and AI. So I want to see this individual
tree and how well it's doing. And sometimes you
have people who still think like satellites and
AI, no, that's not working in forest, forest
is special. It's somewhere in the middle and
that's always really annoying. If we go back
to really technical, I think a lot is possible.
For me, the challenges with working on a large
scale with high resolution data, especially with
time series, are still really annoying because
I know that there's a lot of data out there,
but most of it is commercial and then it tends
to be nuts. available for a lot of the applications
that I feel need it most. You can do a lot if
you want to go into oil and gas, but if you want
to make sure there's a world left, then it's
a constant uphill battle that way. Again, maybe
not the most technical issue, so I think, yeah.
I kind of like the technical challenges. Yeah,
clouds are annoying, but it's part of the fun,
right? Gives us a puzzle to solve. Yeah, whenever
we work with, we have a really wonderful network
of volunteers and sometimes we get satellite
imagery from a satellite image provider that
they request. They'll be working on a case and
they'll go, hey, I need a picture from here.
And if the volunteer, if the individual isn't
familiar with satellite imagery, we have lots
of folks who've never worked with it in the past
and are now working with it for the first time
at BellyCat. you know, 95 % of the time we get
the image, we send it to them and we go, it's
cloudy. Like we could, you know, here's the result,
or you can't see anything. And they go, oh, of
course, like there's clouds. Yeah, there's lots
of clouds. And depending on the place, there's
clouds like every day. So, yeah, definitely sympathize
with that. We did a project in Ivory Coast and
I think in a year's time we could get one image.
Yeah, that sounds about right. Yeah. We have
another question here from another community
member named BQN. And BQN asks, Lisa, what is
the most interesting or innovative or unusual
or creative application of satellite image data
that you have come across? So I kind of want
to talk about this project we've done. It wasn't
technically with satellite image because here
we could get through a partner, really like the
project as well, some aerial data. But basically
for us, the processing is pretty much the same.
It's a bit less atmospheric correction needed,
but data is data, right? So I was working with
aerial data and that was to map out latrines.
Where MREF held Africa, this project, this wash
intervention to support, to give out micro credits
for people to build latrines. And they wanted
to know how effective the project was. Now they
needed to do a comparison to a different site.
to see how many latrines people would normally
build, at least would build us outhouses. And
it's kind of invasive to be knocking on people's
door and ask, hey, can I see your toilets? So
they were looking for a different solution and
then we developed a model to see, hey, where
can we see outhouses and how can we distinguish
latrines from chicken coops, which is very challenging.
And I really like talking about that, like looking
at the different angle, like, yeah, sure, sure,
we can map latrines, why not? Yeah, I don't think
I've ever heard of anything like that. That's
fascinating. Thanks for sharing that. We have
a question here from Aikan. Hello, Aikan. Thanks
for your question. And the question is, do you
have any tips for using platforms like Google
Earth or Sentinel for identifying and confirming
smaller scale environmental sites like artisanal
mines, for example? So artisanal minds, we've
also done some work in. I believe there might
even be some tutorials using it in Google Earth
Engine. Otherwise, what I would really look at
is look for bare soil index. So really look at
bare soil and mapping changes into that. So looking
at the time series analysis, you can look into,
for example, BFAST, B -F -A -S -T. that's normally
done on the NDVI but you can also do it on for
example bare soil index and using that to map
where you see for example bare soils popping
up in certain area sizes so you want to look
at then quite small area sizes where there's
quite a rapid change ideally you make some some
smart selections about your area of interest
where you expect it to happen so for example
close to rivers and then Second point of advice
is don't be afraid to get dirty and just click
through images and look at things. So use these
algorithms. If you don't have a lot of training
data, don't spend too much time on building complex
models. If you don't have the data to put in
there and the model is not going to be great
anyway and click through things, look at the
different images and use your own eyes as well
because our own eyes are very good at change
detection and pattern recognition. Use the computer
to take the first step, but don't try to get
everything done perfectly Rather just look and
click and do the dirty work yourself as well
We have a question now from wit Fox and you've
talked You know, we just talked a little bit
about this. Let's let's see if we can get more
On this Lisa. You've talked a bit about deforestation
biodiversity fires mines. We talked about latrines
a moment ago What are some things that you think
satellites are being underutilized for? Or what
are some other perhaps non -obvious use cases
for satellite imagery data, either optical or
multispectral? Thanks. Take your moment. Because
I know satellite data is being used for loads
of different things. even for mapping penguin
colonies by looking at their shit you can do
with satellite so what they're not being used
for is kind of difficult to look at yeah I think
a lot of places a lot more can be done I think
for mapping these environmental claims for big
organizations I think they can be used a lot
more because I feel like for a lot of these cases
companies will make a claim, for example, in
the offsetting. They will share some sort of
location with it or they will share some sort
of report with it. But the data tends to be based
on field work, which is nice, but which must
be validated. We've seen it a lot where even
companies we work with who do field work professionally
and who do it for the project we asked them,
give us data where there's just errors in there.
That makes me wonder how much validation is being
done to support these claims for, for example,
the regulated carbon market. I know it's being
done there, and I know it's kind of an open and
short case, but I think more can be done there.
Thanks, Lisa. We have about 10 minutes here left
with Lisa Brughausen from Space for Good. If
you have any questions, please type them in the
chat. You've got 10 minutes to ask her. A question,
we've got one here from SirSkippy. And SirSkippy
asks, do you make the data that you collect available
on OpenStreetMap for ECUs by others? No, we do
not. And the data we collect, we don't have our
own satellites. So the data we collect is the
satellite data itself, the raw satellite data.
And the data, the results, the information we
generate We don't make available and I think
so we also work for, for example, commercial
entities to take their direct action. I think
in most of those cases, it would not be beneficial
to them to publish it. And then we also need
to be cautious with that and rather focus on
the direct action and on the mapping. Plus in
OpenStreetMap, if we do, for example, deforestation
detection. So like I said, what we do is we look
at deforestation as there was a forest it's gone
now there's no more forest if you look at most
land cover maps they look at what is the land
cover we the official land cover that's here
the land use that's here so if you cut a forest
and a new forest grows it is still the land cover
class of forest and then there's no rapid change
for change that we detect and then in the other
land cover mapping we tend to have a bit more
classes than are relevant for This road networks
could make sense. But even then I think that
working with the GPS tracker so people have locally
are probably going to be more, uh, more directly
applicable also because it's in private areas.
So they don't want the date. They don't want
people on the roads. So no, we do not. The short
answer. Thanks, Lisa. We got another one here
from, um, uh, this one's from Charlie. Um, are
you Charlie from, from Bellingham? And Charlie
asks, I'm going to paraphrase here. Imagine you're
listening to this and you go, wow, Space for
Good sounds incredible. I think I have some skills
that might make me a candidate. I'm going to
look for postings on the Space for Good website.
Maybe if they're hiring somebody, I'm going to
apply. And the question to you, Lisa, is what
kinds of skill sets do you look for in team members?
Just in case somebody in the audience here wants
to maybe give a shot at working with Space for
Good. Yeah, thanks. So what we're looking for
is really in -depth remote sensing and modeling
skills. So if we look for a data analyst or data
scientist and we look for people who really have
experience in the data, but also have experience
in one or two fields of interest. So we have,
I myself have a mixed background with forestry.
We have someone who has a background in doing
carbon projects in the fields, getting that access
with someone who's done a lot of work around
fires in the past, agriculture. So knowing, and
that's where I think most value lays in, combining
the data sites with direct action in the field
with something, someone who knows the background.
If you don't know how a tree grows, then monitoring
from above is going to be a lot harder and monitoring
it when it's gone is going to be very difficult
to assess. So that's ready for the data. They're
the analytics, they're the scientist position.
Then we have, if we work with, we have, for example,
at the moment, one full stack developer that
was looking for someone with cloud experience,
scalability, et cetera, et cetera. But for us,
the soft side is also very important where you've
got a very cool team, if I may say so, and really
working together as a team to build something
that's better. And then, of course, around commercial,
well, we've got a very small team around that,
but that's also always good to have people who
can try to get it to the final users and clients.
Thanks very much. We have about eight minutes
left here with Lisa. Get your questions in, folks.
We have a question from Dusty Wieners. By the
way, Wieners, if you folks don't know. is the
skin on your elbow is the weenus. I'm not sure
if you are familiar with that term, but Dusty
Weenus is asking the following question. They
say, Dusty Weenus says, is there any way to verify
direct carbon emissions from industrial sites
from space? For example, A company claims a certain
amount of scope one emissions, but the satellite
data actually indicates a larger amount. Is that
possible? For example, methane mapping. Thanks,
Dusty or Venus, I'm not sure how to refer to
you. For example, for methane, there's some satellites
out there that can really map at different resolutions
the direct methane emissions. It's always important
to look at then the full scale of events. If
they report CIRTOSCOPE -1 emissions, you need
to have the correct overpass times, the correct
models behind it. And there's always the issue
that you look with the satellite at the full
column. So at the entire atmosphere, basically.
So there is a potential for a mix -up. However,
these satellites have been used, for example,
to identify when there have been methane leaks
or large -scale methane emissions where they
shouldn't have been due to any technical issues.
So yes, it can be done for larger scale operations.
The farmer next door with 20 cows doesn't exist
anymore. But anyway, you're not going to be able
to find that. But for the large factories, yes.
And for the pipelines where there might be maybe
slight issues with the maintenance as well. I'm
being positive here. Thanks. Thanks Lisa. I think
this is going to be the last question here. Somebody
notices we're talking about, you know, work in
the field and maybe working space for good. Somebody
noticed that there's an astropreneur position
on the website. Are you familiar with that one?
I'm asking it because Chris is asking about this,
but also I've never heard of that term. Do you
know about the position, the vacancy? Can you
talk about it? It might be a word we made up.
More on the data side than that side. So let
me quickly, where is that? It's in the, yeah,
spaceforgo .com forward slash vacancies. Marketing
and sales, market research and analysis, finance
and investment. Oh yeah, talented and engaged
entrepreneurs. So that's basically an open, always
open vacancy where people who are interested
can. let us know and we can see whether there
is there is anything out there uh that always
depends a bit on projects on current needs on
skills whether we can fit something in or not
um lately usually not i have to admit because
we're with quite quite a strong team at the moment
uh but quite frequently we can find figure out
something so um yeah entrepreneur combination
of uh astro and entrepreneur so also looking
at people who want to We do work as well with
a shell of freelancers who take specific projects
or who we support in giving specific projects
when there are specific skill sets we don't have
in the team, but we need in certain projects
or that just want to be somehow connected because
we are a lot of fun. Great. Thank you so much
for that. And we've got this last question here,
folks, again, from SIRS -KP, has the change in
US government funding priorities affected Space
for good at all. We get that question about again
all the time. We're passing it on to you Lisa
space for good. Yeah, you don't have to ask the
book Affected us. I mean emotionally financially
directly or indirectly. I think it's affecting
affecting the entire world At the moment, it
has not affected any of the current of the projects
that we do. And that's also where we try to have
a mix between commercial projects, projects with
NGOs and governmental projects. We do know that
a lot of our partners have been affected quite
strongly and we do see quite some risk here because
the budgets are also affecting the data sets.
And then we're looking at the climatological
data sets, which we use if we make predictive
models. It's important to have those available
globally rather than only in certain regions
because the climate is kind of a global thingy.
So there we do expect it to affect us on the
data set quite a bit and on the projects not
on the active projects we have but on the yeah
future ones unfortunately I think that's yeah
that's great well On that depressing note, we're
going to call it. Thank you so much, Lisa. Brooke
Hellsend from Space for Good. Thanks for your
wonderful presentation. I learned a lot. I know
folks here learned a lot as well. Thank you very
much, Lisa, for spending this hour with us. Thank
you. And have a good evening and day, everyone.
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