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Pixel_level_land_classification ⭐ 166

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

Classification Of Hyperspectral Image ⭐ 73

Classification of the Hyperspectral Image Indian Pines with Convolutional Neural Network

Geograph ⭐ 26

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.

Land Cover Semantic Segmentation Pytorch ⭐ 18

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

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

Landcover Orinoquia ⭐ 12

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

Satsense ⭐ 10

Satsense is a Python library for land use/cover classification using satellite imagery

Semi_automated_obia_processing_with_local_uspo ⭐ 8

This repository contains the computer code of a semi-automated framework for land cover mapping using OBIA and local USPO

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