satellite-image-classification

There are 4 repositories under satellite-image-classification topic.

eye-in-the-sky
manideep2510 / eye-in-the-sky
Satellite Image Classification using semantic segmentation methods in deep learning
remote-sensing deep-learning computer-vision machine-learning keras tensorflow unet pspnet semantic-segmentation artificial-intelligence satellite-images satellite-image-classification
Language:Python 295
artemisart / EuroSAT-image-classification
Satellite images classification
eurosat pytorch image-classification satellite-image-classification
Language:Python 13
GeospatialGeeks / Py4Geo
Satellite Image Analytics and Earth Data Science Experiments in Python
gdal satellite-image-classification satellite-imagery satellite-imagery-analysis satellite-images scikit-image scikit-learn scikitlearn-machine-learning skimage
Language:Jupyter Notebook 7
kkgadiraju / SAT-Classification-Using-CNN
Classifying SAT-6 data using a CNN
satellite-image-classification deep-learning tflearn python
Language:Jupyter Notebook 7
BEEILAB / LULC-Classification-Using-Deep-learning
This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!
cnn deep-learning lulc remote-sensing satellite-image-classification sentinel-2 land-use-land-cover-classification eurosat tensorflow
Language:Jupyter Notebook 5
efejzo20 / CNN-Satellite-Image-Classification
The application of various CNNs on an Aid dataset for satellite image classification.
cnn deep-learning satellite-image-classification
Language:Jupyter Notebook 3
Few-shot-satellite-image-classification-OPS-SAT
ShendoxParadox / Few-shot-satellite-image-classification-OPS-SAT
Few-shot satellite image classification for bringing deep learning on board OPS-SAT
color-transfer data-augmentation deep-learning ensemble-learning few-shot-classification satellite-image-classification synthetic-data-generation
Language:Python 3
Purushothaman-natarajan / eXplainable-AI-for-Image-Classification-on-Remote-Sensing
This repository provides the training codes to classify aerial images using a custom-built model (transfer learning with InceptionResNetV2 as the backbone) and explainers to explain the predictions with LIME and GradCAM on an interface that lets you upload or paste images for classification and see visual explanations.
deep-learning explainable-ai gradcam gradio-interface image-classification lime neural-network remote-sensing satellite-image-classification tensorflow transfer-learning visual-explanations splime
Language:Jupyter Notebook 1
AbhishekRS4 / overhead_mnist
DL model deployment using docker, API deployment with FastAPI, and MLOps using WandB for overhead-mnist dataset
computer-vision deep-learning pytorch satellite-image-classification overhead-image-classification wandb docker fastapi kolmogorov-arnold-networks cnn automatic-mixed-precision-training
Language:Python 0
DavidColasRomanos / Minsait_Land_Classification
Satellite Image Classification
satellite-image-classification
Language:Jupyter Notebook 0
DP8801 / COMP6721-GroupF
ai cnn deeplearning pytorch satellite-image-classification
Language:Jupyter Notebook
tawsifrm / Satellite-Image-Classification
This repository contains code for building a deep learning model to classify satellite images into different categories such as Cloudy, Desert, Green Area, and Water.
cnn kaggle python satellite-image-classification
Language:Python