eurosat

Classifying custom image datasets by creating Convolutional Neural Networks and Residual Networks from scratch with PyTorch

This is the ResNet50 implementation of the Eurosat dataset.

Satellite images classification

ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.

A fast and easy-to-use Remote sensing Image format COnverter for High-throughput Deep-Learning (rico-hdl).

Train Convolutional Neural Network to predict land cover type from multispectral Sentinel-2 satellite imagery

This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!

Interactive web app for land use classification from Sentinel-2 satellite imagery using deep learning.

Federated Learning in Satellite Constellations using Flower

A reproducible cross‑framework study comparing CNN and CNN‑ViT hybrid architectures for EuroSAT satellite crop classification using aligned Keras and PyTorch implementations.

Cross domain few-shot transfer learning from MiniImageNet to EuroSAT_RGB and CUB

WildSAT is an ecological AI system that lets users search satellite imagery using natural language queries like "dense tropical forest" or "wildfire-prone vegetation" via a CLIP-style contrastive pipeline, using a ResNet18 + ViT-B/16 hybrid encoder and DistilBERT as text encoder, visualized on an interactive Mapbox satellite map.

Domain adaptation for satellite image classification using a pretrained EuroSAT ResNet50 model adapted to Sentinel-2 + Dynamic World target data.

DL-LULC-Classifier is a deep learning project for Land Use and Land Cover (LULC) classification using Convolutional Neural Networks (CNNs). It features can support multiple models, easy integration with Django and HTMX as frontend. This tool is ideal for environmental monitoring and geospatial analysis.

Satellite image classification using a custom Convolutional Neural Network (CNN), which achieves 96% accuracy on test data . The model is designed to classify images from the EuroSAT dataset into ten distinct classes.

PyTorch CNN for land use/land cover classification of Sentinel-2 images from the EuroSAT dataset.

A lightweight ensemble deep learning model achieving 98% accuracy on EuroSAT land-cover classification using 4-channel Sentinel-2 data.

Custom TensorFlow training loops for image classification: a foundational CNN on Eurosat using tf.GradientTape for learning, and an optimized MNIST MLP with BatchNorm, Dropout, and learning rate scheduling for higher accuracy.

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.

Satellite land cover classification using Transfer Learning with ResNet50 and PyTorch on the EuroSAT dataset.