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.

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

Klassifikation von Satellitenbildern mit TensorFlow

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.

experiments with DINO method for training vision transformer on EuroSAT dataset

Image classification of EuroSAT dataset with CNN and ResNet

Satellite image classification using a custom Convolutional Neural Network (CNN). The model is designed to classify images from the EuroSAT dataset into ten distinct classes.

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.

🛰️ Easy Access to the EuroSAT Dataset in R

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

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

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

Satellite images classification

This is the ResNet50 implementation of the Eurosat dataset.

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