Land Cover Mapping

🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).

Landscape synthesis for UE4 using landcover maps and digital elevation models.

A Google Earth Engine API (interactive dashboard) for satellite-based global climate hazard analysis (urban heat, landcover changes, etc). Project under World Bank Group. ⬇️ ⬇️

GeoGraph provides a tool for analysing habitat fragmentation and related problems in landscape ecology. GeoGraph builds a geospatially referenced graph from land cover or field survey data and enables graph-based landscape ecology analysis as well as interactive visualizations.

Code for journal paper "Deep Learning for Land Cover Change Detection".

A collection of Imagery Explorer web applications developed by Esri's ArcGIS Living Atlas team

Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project

Object-based land cover classification with Support Vector Machine for Google Earth Engine

Determine land use and land cover classification based on Sentinel-2 satellite images with state-of-the-art performance 🛰

Object-based Image Analysis (OBIA)

A high-level Python framework to evaluate the skill of geospatial datasets by comparing candidates to benchmark maps producing agreement maps and metrics.

2D Convolutional Neural Network for land use and land cover classification of radar and hyperspectral images

An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation

A lightweight and customizable web GIS platform for the state of Goa, India. Designed as a community maintained tool for hyperlocal spatial data exploration and decision making.

This repository hosts a collection of experiments aimed at testing the effectiveness of transcoding Synthetic Aperture Radar (SAR) imagery to optical ones. The focus of these experiments is on solving the challenge of waterbody extraction in arid regions posed by the similarity of the intensity values of waterbodies and sand landcover in SAR image

Simple layers for species distribution modeling and bioclimatic data

a minetest mod for importing elevation and landcover rasters as world maps

A web mapping app to test, tweak and train the land cover classification from a deep neural network model built by @microsoft

Landcover classification using the fusion of HSI and LiDAR data.

Google Earth Engine App - Tamil Nadu LandUse LandCover classification - Random Forest Classifier

We build a challenging cloud detection dataset called AIR-CD, with higher spatial resolution and more representative landcover types.

Achieved a jaccard index of 0.75 with 100 images.LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-2 mission in 2018. Version 1.0 of the dataset contains data across Africa, which accounts for ~1/5 of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice. There are a total of 1980 image chips of 256 x 256 pixels in V1.0 spanning 66 tiles of Sentinel-2. Each image chip contains temporal observations from Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution and an annual class label, all stored in a raster format (GeoTIFF files).

Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.

Code for spatially and temporally generalizable regional land cover mapping

Multi-label Land Cover Classification with Deep Learning

Worked on multispectral images to analyze and predict the Land use (human use of land such as constructions) and Land cover (Natural cover of land such as vegetation, water bodies etc) changes occurred over a period of time in the concerned location using Deep Learning (LSTMs and Fuzzy C-means clustering)

Make low resolution landcover vector tiles from high resolution raster landcover data with h3 downsampling

Sentinel-1 SAR landcover classification analysis in Google Earth Engine (JavaScript)

Scalable approach for high-resolution land cover in Python