image.classification.on.EuroSAT -> solution in pure pytorch hurricane_damage -> Post-hurricane structure damage assessment based on aerial imagery with CNN openai-drivendata-challenge -> Using deep learning to classify the building material of rooftops (aerial imagery from South America) is-it-aband...
Sat6405,000 image patches each of size 28x28 and covering 6 landcover classes -barren land, trees, grassland, roads, buildings and water bodies. Deep Gradient Boosted Learning article Kaggle - other Satellite + loan data ->https://www.kaggle.com/reubencpereira/spatial-data-repo ...
Deep learningNeural networksIn this chapter we highlight how rapid advances in computer vision and the increasing availability of high-resolution satellite imagery have facilitated more accurate, efficient, and scalable environmental monitoring and regulation. First, we highlight the range of potential ...
Satellite imageDeep learningConvolution Neural Networks (CNNs)UC-Merceed Land UseParallel computingNowadays, large amounts of high resolution remote-sensing images are acquired daily. However, the satellite image classification is requested for many applications such as modern city planning, agriculture ...
In part 1, we discussed how to export training data for deep learning using ArcGIS python API. In part 2, we demonstrated how to prepare the input data, train a feature classifier model, visualize the results, as well as apply the model to an unseen image. Then we covered how to ...
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks 2021, Proceedings of the IEEE International Conference on Computer Vision A Review of Deep Learning in Multiscale Agricultural Sensing 2022, Remote Sensing View all citing articles on ScopusView...
we highlight asample projectof using Azure infrastructure for training a deep learning model to gain insight from geospatial data. Such tools will finally enable us to accurately monitor and measure the impact of our solutions to problems such as deforestation and human-wildlife conflict, h...
This paper present class activation mapping in conjunction with transfer learning to investigate and explain the predictions of a deep learning based satellite image classification. Deep learning based classifiers offer no way of gauging what a network has learned or which part of an input to the ...
Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and ...
Annotation of datasets for deep learning applied to satellite and aerial imagery - satellite-image-deep-learning/annotation