Geospatial Deep Learning resources & study group

(Dave Luo) #1

Hi,

Inspired by people’s projects & interest in geospatial analysis (and following the excellent example of the active & interesting time series/sequential data study group), here is a wiki post & dedicated thread for us to come together in sharing know-how, questions, project collaborations, & new ideas for applying cutting edge deep learning with fast.ai to improve our geospatial understanding of the world.

To kick things off, here are some starter lists of resources and recent relevant posts. This is now a wiki post so please feel free to edit to add more:

Knowledge Base

Projects & achievements by Fast.ai students

(non-exhaustive, please add any I’ve missed):

Data sources:

Active & recent competitions:

This challenge focuses on the use of Off-Nadir imagery for building footprint extraction. The dataset includes 27 WorldView 2 Satellite images from 7 degrees to 54 degrees off-nadir all captured within 5 minutes of each other. The dataset covers over 665 square kilometers of downtown Atlanta and ~126,747 buildings footprints labeled from a nadir image.

Cited papers & other references:

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Deep Learning in Medicine resources & Study Group
(Henri Palacci) #2

Wonderful idea, thanks for creating this most excellent wiki!

I would be especially interested in brainstorming with others about “non-competition” ideas with potential real-world value. I’m thinking something along the lines of drought watch?

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(RobG) #3

Great idea.

Remote Sensing is a rich seam of DL potential.

There was also a project by @divyansh to detect swimming pools IIRC. (I couldn’t work out how to amend a wiki post).

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(Francisco Ingham) #4

I would be very interested in collaborating Henri.

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(Pierre Ouannes) #5

I would be very interested by working on that as well !

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#6

This would be a super interesting challenge to work on! One could use the approach @daveluo used for building detection on drone images with the extra challenge of doing DL for images with more than 3 channels!

There are 3 medium blogposts about the challenge describing the dataset, its unqiue challenges and there are also some pretrained models available -> 1 // 2 // 3

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(Ilia) #7

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(Henri Palacci) #8

@lesscomfortable @PierreO Great! I would suggest we all throw ideas here :sunny:

Here’s a good listicle with a couple of free-ish satellite imagery resources. In the end I ended up not using them in my previous project, because I’m used to read google API docs and went with the Google maps static API.

That said, the main constraint I can think of for all “relevant” projects is with the availability and temporality of the data at inference time. Training data should be plentiful :slight_smile:

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(Christian Werner) #9

Nice! Some Geo-Folks around it seams…

Has anyone here worked with Sentinel data? I’m interested in leveraging Sentinel1+2 (radar+optical)…

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(Francisco Ingham) #10

Coming from Argentina I am focused in agriculture:

  • Predicting commodity pricing from crop hectares in the world (crazy but we can filter out later)
  • Predicting plantation yields/counting crops (as per Dave Luo)
  • Identifying pests and harm to vegetation health to attack them quickly
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(Pierre Ouannes) #11

Predicting weather, be it a particular event like drought as you suggested @henripal, or just global weather is very interesting to me.
It’s not very far from agriculture either. I’m curious of how deep learning could fare in such a chaotic system.

I would love a project around counting crop as well, as you suggested @lesscomfortable. I’m really interested by image segmentation right now so it would fit right in !

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#12

I have worked with Sentinel 1+2 data for my master thesis where I used Random Forests + Support Vector Machine to do Landcover Classification. Would be interesting to see, what one could do with DL :slight_smile:

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(Christian Werner) #13

Nice. Is your thesis available online somewhere? Would love to take a look…

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#14

The whole thesis is not available as I got a bit distracted finishing it, but I have a git repo where most of the code and a conference presentation are included:

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(RobG) #15

I enjoyed taking part in a US midwest soy/corn segmentation problem earlier this year. https://www.crowdanalytix.com/contests/agricultural-crop-cover-classification-challenge

There is huge potential in using LANDSAT + annotations from https://nassgeodata.gmu.edu/CropScape/ to train other cnn’s for other crops/locations (US) or anywhere else that has both imagery and annotations. If anything, choosing problems that have readymade annotations is my recommendation.

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(rares t) #16

i practiced binary image segm on INRIA dataset. with no modifications to u-net i obtained 0.96 accuracy, 0.77 iou and 0.87 dice.
i found the spacenet competition a few weeks ago. i am glad we are doing this here.

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(Henri Palacci) #17

Along those lines, I came across the Global Forest Watch website - especially given the situation in California, some automated forest watcher sound like it would be useful.

Re: @digitalspecialists’s point, it seems like GFW has a fair amount of labeled data. Not sure if enough.

Descartes labs made a splash a couple years back with their DL crop yield prediction - but it didn’t seem to stand the test of time.

Anyway, I propose to select a couple of these ideas, and split the responsibility of fleshing them out? (data availability, usefulness, feasability, previous attempts, etc…)

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(Pierre Ouannes) #18

How about a small poll to see where’s everyone at ? I think that’s all the ideas we currently mentioned but if there’s others you want me to add no problem !

  • Predicting commodity pricing
  • Predicting plantation yields/counting crops
  • Identifying pests and harm to vegetation
  • Predicting weather

0 voters

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(Dave Luo) #19

Back online & catching up from Thanksgiving holiday travel, it’s great to see the excitement, resources, & project thoughts shared so far!

I’ve updated the original post to include all links/resources cited so far. The post has now been been wikified so please feel free to edit/add directly.

Re:

in addition to what’s been suggested so far, I’ll add a few complementary ideas under the big umbrella of Planetary Health (defined by the Lancet as the "health of human civilisation and the state of the natural systems on which it depends”):

In terms of resources, I’ve added a few new links to the wiki:

Also for accessing remote sensing data, a new version of this just came out (haven’t had a chance to try it out yet but looks promising): https://github.com/pytroll/satpy

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(Miguel) #20

Great topic! I’m also working with remote sensing data, focusing now on the wildfires problem. There are many datasets available on Google EarthEngine (like Sentinel 1, 2, 3, 5, Lansat, VIIRS, Modis, and even weather forecasts from GFS), updated regularly. It can be useful to preprocess large datasets and only download the final images.

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