An interactive jupyter notebook for downloading satellite imagery
Project description
Seg2Map :mag_right: :milky_way:
An interactive web map app for applying Doodleverse/Zoo models to geospatial imagery
Overview:
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Seg2Map facilitates application of Deep Learning-based image segmentation models and apply them to high-resolution (~1m or less spatial footprint) geospatial imagery, in order to make high-resolution label maps. Please see our wiki for more information.
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The principle aim is to generate time-series of label maps from a time-series of imagery, in order to detect and assess land use/cover change. This project also demonstrates how to apply generic models for land-use/cover on publicly available high-resolution imagery at arbitrary locations.
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Imagery comes from Google Earth Engine via s2m_engine. Initially, we focus on NAIP time-series, available for the conterminious United States since 2003. In the future, Planetscope imagery may also be made available (for those with access, such as federal researchers).
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We offer a set of Segmentation Zoo models, especially created and curated for this project based on a publicly available datasets. These datasets have been selected because they are public, large (several hundred to several thousand labeled images), and provide broad class labels for generic land use/cover mapping needs.
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
Contributions:
We welcome collaboration! Please use our Discussions tab if you're interested in this project. We welcome user-contributed models! They must be trained using Segmentation Gym, and then served and documented through Segmentation Zoo - get in touch and we'll walk you through the process!
Roadmap / progress
V1
- Develop codes to create a web map for navigation to a location, and draw a bounding box
- Develop codes interface for controls (time period, etc)
- Develop codes for downloading NAIP imagery using GEE
- Put together a prototype jupyter notebook for web map, bounding box, and image downloads
- Create Seg2Map models
- Coast Train / aerial / high-res. sat
- 2 class dataset (water, other)
- zenodo release for 768x768 imagery zenodo page
- Coast Train / NAIP
- 5 class dataset (water, whitewater, sediment, bare terrain, other terrain)
- 8 class dataset (water, whitewater, sediment, bare terrain, marsh veg, terrestrial veg, ag., dev.)
- zenodo release of 5-class ResUNet models for 768x768 imagery zenodo page
- zenodo release of 8-class ResUNet models for 768x768 imagery zenodo page
- zenodo release of 5-class Segformer models for 768x768 imagery zenodo page
- zenodo release of 8-class Segformer models for 768x768 imagery zenodo page
- Chesapeake Landcover (CCLC) / NAIP
- EnviroAtlas / NAIP
- 6 class dataset (water, impervious, barren, trees, herbaceous, shrubland)
- zenodo release of 6-class ResUNet models for 1024 x 1024 models zenodo page
- OpenEarthMap / aerial / high-res. sat
- 9 class dataset (bareland, rangeland, dev., road, tree, water, ag., building, nodata)
- zenodo release of 9-class ResUNet models for 512x512 models zenodo page
- DeepGlobe / aerial / high-res. sat
- 7 class dataset (urban, ag., rangeland, forest, water, bare, unknown)
- zenodo release for of 7-class ResUNet models 512x512 imagery zenodo page
- Barrier Islands / 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
- Geomorphic setting data 7 class (beach, backshore, dune, washover, barrier interior, marsh, ridge/swale)
- zenodo release of Geomorphic setting models for 768x768 imagery
- 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
- AAAI / aerial / high-res. sat
- 2 class dataset (other, building)
- zenodo release for 1024x1024 imagery zenodo page
- 2 class dataset (other, flooded building)
- zenodo release for 1024x1024 imagery zenodo page
- xBD-hurricanes / aerial / high-res. sat, a subset of the XView2 dataset
- 4 class building dataset (other, no damage, minor damage, major damage)
- zenodo release for 768x768 imagery zenodo page
- 2 class building dataset (other, building)
- zenodo release for 768x768 imagery zenodo page
- Superclass models
- 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
- Coast Train / aerial / high-res. sat
- 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 3m imagery available via Planet API (federal researchers only)
- Include additional models/datasets (TBD)
Datasets
General Landcover
DeepGlobe
- paper
- challenge
- data
- 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
- EnviroAtlas paper
- paper using EnviroAtlasdata
- This dataset was organized to accompany the 2022 paper, Resolving label uncertainty with implicit generative models. More details can be found here
- 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
- data
- paper
- 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
- 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
- website
- data
- preprint
- 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
- data
- paper
- 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
- XBD-hurricanes code
- 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
- paper
- 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
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 |
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