Remote Sensing Sar-Optical Land-use Classfication Pytorch Pytorch高分辨率遥感语义分割/地物分割/地物分类
Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the earth from June, 2015. This image patches can be trained and classified using transfer learning techniques.
ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.
Classification of land based on land cover data.
Optimizing Deep Learning Models for Classifying Urban Land Cover.
Harvest data from Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) part of the Australian Department of Agriculture, Fisheries and Forestry and the Australian Bureau of Statistics (ABS) for your work in R
Satellite Image Classification using CNN - EuroSAT Dataset Deep Learning model achieving 96.4% accuracy in land use recognition TensorFlow implementation with comprehensive analysis and visualizations
Implementation for "Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation"
Learn how to avoid pitfalls and enhance your Land Use and Land Cover classification results with a comprehensive workflow
Online documentation for Collect Earth Online.
Project for the Deep Learning course taught at Wageningen University
A machine learning project for satellite image classification using the EuroSAT dataset. Implements classical ML approaches with handcrafted features (HOG, LBP, edge detection) to classify 10 land-use types from Sentinel-2 imagery, demonstrating competitive performance without deep learning.
A TensorFlow/Keras U-Net pipeline for land-use segmentation of Central Phoenix Sentinel-2 imagery (2022–2025) with Dynamic World labels, featuring patch-based training, evaluation notebooks, and high-accuracy visualizations.
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