Restore entire Readme

Doodleverse · Oct 6, 2023 · 7aae354 · 7aae354
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@@ -94,10 +94,279 @@ First, you need to request access to Google Earth Engine at https://signup.earth
   ```
 
 ## Features
-
 ### 1. Download Imagery from Google Earth Engine
-
 Use google earth engine to download multiple years worth of imagery.
 ![download_imagery_demo](https://github.com/Doodleverse/seg2map/assets/61564689/a36421de-e6d2-4a3f-8c08-2e47be99e3e0)
 
-### You can download multiple ROIs and years of data at lighting speeds 🌩�
+### You can download multiple ROIs and years of data at lighting speeds 🌩️
+
+![download_imagery_demo_multi_roi](https://github.com/Doodleverse/seg2map/assets/61564689/46219ca8-beed-46e0-a28f-0d5ceab6d474)
+
+
+### 2. Apply Models to Imagery
+
+![apply_model_demo](https://github.com/Doodleverse/seg2map/assets/61564689/75c55659-56f4-46f3-892d-6bebdcd6a653)
+
+
+### 3. Load Segmented Imagery onto the Map
+
+![load_segmentation_demo](https://github.com/Doodleverse/seg2map/assets/61564689/d6bbf3ba-a8a9-4e90-b47a-61c9c2fe4799)
+
+## Generic workflow:
+* Provide a web map for navigation to a location, and draw a bounding box
+* Provide an interface for controls (set time period, etc)
+* Download geospatial imagery (for now, just NAIP)
+* Provide tools to select and apply a Zoo model to create a label image
+* Provide tools to interact with those label images (download, mosaic, merge classes, etc)
+
+## Authors
+* [@dbuscombe-usgs](https://github.com/dbuscombe-usgs)
+* [@2320sharon](https://github.com/2320sharon)
+
+Contributions:
+* [@venuswku](https://github.com/venuswku)
+
+We welcome collaboration! Please use our [Discussions](https://github.com/Doodleverse/seg2map/discussions) tab if you're interested in this project. We welcome user-contributed models! They must be trained using [Segmentation Gym](https://github.com/Doodleverse/segmentation_gym), and then served and documented through [Segmentation Zoo](https://github.com/Doodleverse/segmentation_zoo) - get in touch and we'll walk you through the process!
+
+## Roadmap / progress
+
+### V1 
+- [X] Develop codes to create a web map for navigation to a location, and draw a bounding box
+- [X] Develop codes interface for controls (time period, etc)
+- [X] Develop codes for downloading NAIP imagery using GEE
+- [X] Put together a prototype jupyter notebook for web map, bounding box, and image downloads
+- [ ] Create Seg2Map models
+  - [X] [Coast Train](https://coasttrain.github.io/CoastTrain/) / aerial / high-res. sat
+    - [X] 2 class [dataset](https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data) (water, other)
+    - [X] zenodo release for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7574784) 
+  - [X] [Coast Train](https://coasttrain.github.io/CoastTrain/) / NAIP
+    - [X] 5 class [dataset](https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data) (water, whitewater, sediment, bare terrain, other terrain)
+    - [X] 8 class [dataset](https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data) (water, whitewater, sediment, bare terrain, marsh veg, terrestrial veg, ag., dev.)
+    - [X] zenodo release of 5-class ResUNet models for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7566992)   
+    - [X] zenodo release of 8-class ResUNet models for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7570583)   
+    - [X] zenodo release of 5-class Segformer models for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7641708)   
+    - [X] zenodo release of 8-class Segformer models for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7641724)      
+  - [X] [Chesapeake Landcover](https://lila.science/datasets/chesapeakelandcover) (CCLC) / NAIP
+    - [X] 7 class [dataset](https://lila.science/datasets/chesapeakelandcover) (water, tree canopy / forest, low vegetation / field, barren land, impervious (other), impervious (road), no data)
+    - [X] zenodo release of 7-class ResUNet models for 512x512 imagery [page](https://doi.org/10.5281/zenodo.7576904)
+    - [X] zenodo release of 7-class SegFormer models for 512x512 imagery [page](https://doi.org/10.5281/zenodo.7677506)    
+  - [X] [EnviroAtlas](https://zenodo.org/record/6268150#.Y9H3vxzMLRZ) / NAIP 
+    - [X] 6 class dataset (water, impervious, barren, trees, herbaceous, shrubland)
+    - [X] zenodo release of 6-class ResUNet models for 1024 x 1024 models [zenodo page](https://doi.org/10.5281/zenodo.7576909)
+  - [X] [OpenEarthMap](https://open-earth-map.org/) / aerial / high-res. sat
+    - [X] 9 class [dataset](https://zenodo.org/record/7223446#.Y9IN2BzMLRY) (bareland, rangeland, dev., road, tree, water, ag., building, nodata)
+    - [X] zenodo release of 9-class ResUNet models for 512x512 models [zenodo page](https://doi.org/10.5281/zenodo.7576894) 
+  - [X] [DeepGlobe](https://arxiv.org/abs/1805.06561) / aerial / high-res. sat
+    - [X] 7 class [dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset) (urban, ag., rangeland, forest, water, bare, unknown)
+    - [X] zenodo release for of 7-class ResUNet models 512x512 imagery [zenodo page](https://doi.org/10.5281/zenodo.7576898) 
+  - [ ] [Barrier Islands](https://www.sciencebase.gov/catalog/item/5d5ece47e4b01d82ce961e36) / orthomosaic / coastlines
+    - [ ] Substrate data 6 class (dev, sand, mixed, coarse, unknown, water)
+    - [ ] zenodo release of substrate models for 768x768 imagery  
+    - [ ] Vegetation type data 7 class (shrub/forest, shrub, none/herb., none, herb., herb./shrub, dev)
+    - [ ] zenodo release of Vegetation type  models for 768x768 imagery   
+    - [ ] Vegetation density data 7 class (dense, dev., moderate, moderate/dense, none, none/sparse, sparse)
+    - [ ] zenodo release of Vegetation density  models for 768x768 imagery  
+    - [X] Geomorphic setting data 7 class (beach, backshore, dune, washover, barrier interior, marsh, ridge/swale)
+    - [ ] zenodo release of Geomorphic setting  models for 768x768 imagery  
+    - [X] Supervised classification data 9 class (water, sand, herbaceous veg./low shrub, sparse/moderate, herbaceous veg/low shrub, moderate/dense, high shrub/forest, marsh/sediment, marsh/veg, marsh, high shrub/forest, development)
+    - [ ] zenodo release of Supervised classification models for 768x768 imagery      
+  - [X] [AAAI](https://github.com/FrontierDevelopmentLab/multi3net) / aerial / high-res. sat
+    - [X] 2 class dataset (other, building)
+    - [X] zenodo release for 1024x1024 imagery [zenodo page](https://doi.org/10.5281/zenodo.7607895)
+    - [X] 2 class dataset (other, flooded building)
+    - [X] zenodo release for 1024x1024 imagery [zenodo page](https://doi.org/10.5281/zenodo.7613106)
+  - [X] xBD-hurricanes / aerial / high-res. sat, a subset of the [XView2](https://xview2.org/) dataset
+    - [X] 4 class building dataset (other, no damage, minor damage, major damage)
+    - [X] zenodo release for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7613175)
+    - [X] 2 class building dataset (other, building)
+    - [X] zenodo release for 768x768 imagery [zenodo page](https://doi.org/10.5281/zenodo.7613212)
+  - [ ] Superclass models
+    - [X] 8 merged datasets for 8 separate superclass models (water, sediment, veg, herb. veg., woody veg., impervious, building, agriculture)
+    - [ ] zenodo release for 768x768 imagery / water
+    - [ ] zenodo release for 768x768 imagery / sediment
+    - [ ] zenodo release for 768x768 imagery / veg
+    - [ ] zenodo release for 768x768 imagery / herb. veg.
+    - [ ] zenodo release for 768x768 imagery / woody veg.
+    - [ ] zenodo release for 768x768 imagery / impervious
+    - [ ] zenodo release for 768x768 imagery / building
+    - [ ] zenodo release for 768x768 imagery / agriculture    
+- [ ] Develop codes/docs for selecting model
+- [ ] Develop codes/docs for applying model to make label imagery
+- [ ] Tool for mosaicing labels
+- [ ] Tool for downloading labels in geotiff format
+
+### V2 
+- [ ] Tool for post-processing/editing labels
+- [ ] Tool for detecting change
+- [ ] Make [Planetscope](https://developers.planet.com/docs/data/planetscope/) 3m imagery available via Planet API (federal researchers only)
+- [ ] Include additional models/datasets (TBD)
+
+## Datasets
+
+### General Landcover
+
+#### DeepGlobe
+* [paper](https://arxiv.org/abs/1805.06561)
+* [challenge](http://deepglobe.org/challenge.html)
+* [data](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset)
+* Zenodo model release (512x512): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7576898
+
+#### EnviroAtlas
+* [EnviroAtlas dataset](https://zenodo.org/record/6268150#.Y9H3vxzMLRZ)
+* [EnviroAtlas paper](https://www.mdpi.com/2072-4292/12/12/1909)
+* [paper using EnviroAtlasdata](https://arxiv.org/pdf/2202.14000.pdf)
+* This dataset was organized to accompany the 2022 paper, [Resolving label uncertainty with implicit generative models](https://openreview.net/forum?id=AEa_UepnMDX). More details can be found [here](https://github.com/estherrolf/qr_for_landcover)
+* Zenodo model release (512x512): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7576909
+
+#### OpenEarthMap
+* [website](https://open-earth-map.org/)
+* [data](https://zenodo.org/record/7223446#.Y7zQLxXMK3A)
+* [paper](https://arxiv.org/abs/2210.10732)
+* Zenodo model release (512x512): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for OpenEarthMap/9-class segmentation of RGB 512x512 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7576894
+
+
+### Coastal Landcover
+
+#### Chesapeake Landcover
+* [webpage](https://lila.science/datasets/chesapeakelandcover)
+* Zenodo model release (512x512): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7576904
+* Zenodo SegFormer model release (512x512): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7677506
+
+#### Coast Train
+* [paper](https://www.nature.com/articles/s41597-023-01929-2)
+* [website](https://coasttrain.github.io/CoastTrain/)
+* [data](https://cmgds.marine.usgs.gov/data-releases/datarelease/10.5066-P91NP87I/)
+* [preprint](https://eartharxiv.org/repository/view/3560/)
+* Zenodo model release, 2-class (768x768): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain water/other segmentation of RGB 768x768 orthomosaic images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7574784
+* Zenodo model release, 5-class (768x768): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7566992
+* Zenodo model release, 8-class (768x768): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7570583
+* Zenodo SegFormer model release, 5-class (768x768): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7641708
+* Zenodo SegFormer model release, 8-class (768x768): Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7641724
+
+#### AAAI / Buildings / Flooded Buildings
+* [data](https://github.com/FrontierDevelopmentLab/multi3net)
+* [data](https://github.com/orion29/Satellite-Image-Segmentation-for-Flood-Damage-Analysis)
+* [paper](https://arxiv.org/pdf/1812.01756.pdf)
+* Zenodo model release (1024x1024) building / no building: Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7607895
+* Zenodo model release (1024x1024) flooded building / no flooded building: Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7622733
+
+#### XBD-hurricanes
+* [Xview2 challenge](https://xview2.org/)
+* [XBD-hurricanes code](https://github.com/MARDAScience/XBD-hurricanes)
+* Zenodo SegFormer model release (768x768) building damage: Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/damaged buildings in RGB 768x768 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7613175
+* Zenodo SegFormer model release (768x768) building presence/absence: Buscombe, Daniel. (2023). Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7613212
+
+#### Barrier Islands
+* [webpage](https://www.sciencebase.gov/catalog/item/5d5ece47e4b01d82ce961e36)
+* [paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0209986)
+* Zenodo Substrate model release (768x768): 
+* Zenodo Vegetation type model release (768x768):
+* Zenodo Vegetation density model release (768x768):
+* Zenodo Geomorphic model release (768x768):
+* Zenodo Supervised classification model release (768x768):
+
+
+## Superclasses
+
+A. Water:
+  * Coast Train
+  * Chesapeake
+  * EnviroAtlas
+  * OpenEarthMap
+  * DeepGlobe
+  * Barrier Substrate
+  * NOAA
+  * [v2: Barrier Substrate]
+  * [v2: Elwha]
+
+B. Sediment:
+  * Coast Train
+  * NOAA
+  * [v2: Barrier Substrate (sand, mixed, coarse)]
+  * [v2: Elwha]
+
+C. Bare:
+  * Chesapeake (barren land)
+  * EnviroAtlas (barren)
+  * OpenEarthMap (bareland)
+
+D. Vegetated:
+  * Coast Train (marsh veg, terrestrial veg, ag)
+  * FloodNet (tree, grass)
+  * Chesapeake (tree canopy / forest, low vegetation / field)
+  * EnviroAtlas (trees, herbaceous, shrubland)
+  * OpenEarthMap (rangeland, tree, ag)
+  * DeepGlobe (ag., rangeland, forest)
+  * NOAA (veg)
+  * [v2: Elwha]  
+  * [v2: Barrier Substrate]
+
+E. Impervious:
+  * FloodNet (Building-flooded, Building-non-flooded, Road-flooded, Road-non-flooded, vehicle)
+  * Chesapeake (impervious (other), impervious (road))
+  * EnviroAtlas (impervious)
+  * OpenEarthMap (dev, road, building)
+  * DeepGlobe (urban)
+  * NOAA (dev)
+  * [v2: Elwha]
+
+F. Building:
+  * OpenEarthMap (building)
+  * AAAI (building)
+
+G. Agriculture:
+  * OpenEarthMap (ag)
+  * DeepGlobe (ag)
+
+H. Woody Veg:
+  * FloodNet (tree)
+  * Chesapeake (tree canopy / forest)
+  * EnviroAtlas (trees)
+  * OpenEarthMap (tree)
+  * DeepGlobe (forest)
+  * [v2: Elwha]  
+  * [v2: Barrier Substrate]
+
+## References
+
+### Notes
+* [NLCD classes](https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description)
+* [NAIP imagery](https://doi.org/10.5066/F7QN651G)
+
+
+## Classes: 
+
+| | Coast Train 1 |  Coast Train 2 |  Coast Train 3| FloodNet | Chesapeake| EnviroAtlas| OpenEarthMap| DeepGlobe| AAAI | NOAA | Barrier Substrate  |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|A. Water | X| X|X |X |X |X |X |X | | X|X|
+|a. whitewater | |X |X | | | | | | | | |
+|a. pool | | | |X | | | | | | | |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|B. Sediment | | X|X | | | | | | | X| |
+|b. sand | | | | | | | | | | |X|
+|b. mixed | | | | | | | | | | | X|
+|b. coarse | | | | | | | | | | | X|
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|C. Bare/barren| |X |X | |X |X | X| X| | | |
+|---|---|---|---|---|---|---|---|---|---|---|
+|d. marsh | | |X | | | | | | | | |
+|d. terrestrial veg| | |X | | | | | | | X| |
+|d. agriculture| | | X| | | |X | X| | | |
+|d. grass | | | |X | | | | | | | |
+|d. herbaceous / low vegetation / field | | | | | X|X | | | | | |
+|d. tree/forest | | | |X |X |X | X|X | | | |
+|d. shrubland | | | | | |X | | | | | |
+|d. rangeland | | | | | |X | X| X| | | |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|E. Impervious/urban/developed | | |X | | |X | X| X| | X| X|
+|e. impervious (other) | | | | |X | | | | | | |
+|e. impervious (road) | | | | |X | | X| | | | |
+|e. Building-flooded | | | | X| | | | | | | |
+|e. Building-non-flooded | | | |X | | |X | | X| | |
+|e. Road-flooded | | | |X | | | | | | | |
+|e. Road-non-flooded | | | |X | | | | | | | |
+|e. Vehicle | | | |X | | | | | | | |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+|X. Other | X| X| | | | | | | X| | |
+
+