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Track 2: land cover classification with low and hi-res labels, top 3 winners.Track 1: land cover classification with low-res labels only, top 4 declared winners.Dataset comprised of 180k triplet patches of Sentinel-1 SAR data, Sentinel-2 multispectral, and MODIS-derived coarse-resolution land cover labels sampled globally and across all 4 seasons.$3K for top 3 Responsible AI for disaster risk management ideasĪctive until 3/6 (track 1) and 3/20 (track 2): 2020 IEEE GRSS Data Fusion Contest global land cover mapping with weak supervision.$12K for top 3 open-source building footprint segmentation models.Dataset comprised of 72 RGB drone image orthomosaics (ranging from 3-20cm/pixel resolution, covering 419 sqkm total) and 790k building footprint labels at two quality level tiers from 10 African cities/regions.Google Earth Engine: Earth Engine Data Catalog | Google DevelopersĪctive until 3/16: Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience.USDA CropLand Data: CropScape - NASS CDL Program.
#Spacenet buildinglabels example free#
listicle of 15 free satellite data sources: 15 Free Satellite Imagery Data Sources - GIS Geography. A comprehensive & actively maintained list of geospatial data competitions & their datasets: GitHub - chrieke/awesome-satellite-imagery-datasets: 🛰️ List of satellite image training data. #Spacenet buildinglabels example code#
published paper and code about BA-Net (“burned areas neural network”) which can use multi-day sequences of multi-spectral imagery to map and date burned areas. 2nd place finish and 3rd place finish in the WiDS Datathon 2019!. 2nd place finish in CrowdAnalytix’s “ Agricultural Crop Cover Classification Challenge": Deep Learning Applications in Agriculture. & Blog post on swimming pool detection and classification. GitHub - daveluo/zanzibar-aerial-mapping: Open source notebooks to create state-of-the-art detecti building segmentation & classification from drone imagery in Zanzibar project:. & 14th place (top 2%!) finish on Kaggle’s Airbus Ship Detection Challenge: Share your work here. “Your City from Space” satellite image project: Share your work here. (non-exhaustive, please add any I’ve missed): Projects & achievements by Fast.ai students posted here, mapping solar PV globally using ML at Open Climate Fix. posted here, Esri’s new R&D center in New Dehli focused on geoDL:. a complete workflow tutorial of building segmentation over drone imagery in Zanzibar with fastai v1 + latest geodata tools: conceptual overview Medium post and interactive code notebook on Colab. Solaris - Geospatial Machine Learning Analysis Toolkit by Cosmiq Works: GitHub - CosmiQ/solaris: CosmiQ Works Geospatial Machine Learning Analysis Toolkit. Raster Vision - an open source framework for deep learning on satellite and aerial imagery by Azavea: GitHub - azavea/raster-vision: An open source framework for deep learning on satellite and aerial. Robosat - semantic segmentation on aerial and satellite imagery: GitHub - mapbox/robosat: Semantic segmentation on aerial and satellite imagery. #Spacenet buildinglabels example series#
Chris Holmes’ excellent and extensive blog series on “Cloud Native Geospatial”: Latest stories and news about Cloud Native Geospatial - Medium.
#Spacenet buildinglabels example how to#
The convention of Slippy Map and how to use it to get geographic coordinates from local map tile image: Share your work here ✅ - #585 by daveluo. U Washington’s GeoHackWeek tutorial series on geospatial data science tools: Geohackweek 2019. This is now a wiki post so please feel free to edit to add more: Knowledge Base and Tools: To kick things off, here are some starter lists of resources and recent relevant posts. 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.
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