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.

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

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

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

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

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.

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

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.

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.

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

Klassifikation von Satellitenbildern mit TensorFlow

experiments with DINO method for training vision transformer on EuroSAT dataset

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.

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