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).

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

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

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

Federated Learning in Satellite Constellations using Flower

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

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.

🛰️ Easy Access to the EuroSAT Dataset in R

This repository contains three different models (ResNet-18, ResNet-50, and ViT-Base-Patch16-224) fine-tuned on the EuroSAT dataset, along with their performance comparisons.

Satellite environmental track of the TUM.ai Makeathon. Team bonk.

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

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

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.

Trained a ResNet50 model on the EuroSAT satellite imagery dataset w/ PyTorch. Analyzed the model's encoder by visualizing linear interpolations within the embedding space to illustrate the semantic separation in the learned feature representations.

Klassifikation von Satellitenbildern mit TensorFlow

This is a Repository used for getting insights about EuroSat dataset and also for training a model in order to classify those 10 classes

experiments with DINO method for training vision transformer on EuroSAT dataset

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.

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 Geo-AI engine for automated ESG and supply chain monitoring. This project uses a ResNet-101 model, fine-tuned on the EuroSAT satellite dataset, to classify land use and detect environmental risks like deforestation, helping enterprises meet regulatory requirements and enhance transparency.

Multiclass image classification of the EuroSAT satellite image dataset with convolutional neural network (CNN) and a pre-trained residual network (ResNet50) using transfer learning

A comparative analysis of state-of-the-art CNN and transformer architectures for an image classification problem.

SKKU AI Introduction Assignment 2