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Seg2Map 🔎 🌌

An interactive web map app for applying Doodleverse/Zoo models to geospatial imagery

Overview:

  • 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.

  • 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.

  • 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).

  • 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
    • 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
      • 7 class dataset (water, tree canopy / forest, low vegetation / field, barren land, impervious (other), impervious (road), no data)
      • zenodo release of 7-class ResUNet models for 512x512 imagery page
      • zenodo release of 7-class SegFormer models for 512x512 imagery page
    • 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
  • 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

EnviroAtlas

OpenEarthMap

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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Seg2Map is an interactive web map app for geospatial image segmentation using deep learning

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