Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset.Vision Transformers Use Case: Satellite Image Classification without CNNs.Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier with repo.Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras or FastAI.Also checkout Multi-label Land Cover Classification using the redesigned multi-label Merced dataset with 17 land cover classes Land Use Classification on Merced dataset using CNN in Keras.Land classification on Sentinel 2 data using a simple sklearn cluster algorithm or deep learning CNN.In general, aerial images cover large geographical areas that include multiple classes of land, so treating this is as a classification problem is less common than using semantic segmentation. This approach of image level classification is not to be confused with pixel-level classification which is called semantic segmentation. The more complex case is applying multiple labels to an image. The classic cats vs dogs image classification task, which in the remote sensing domain is used to assign a label to an image, e.g. Good background reading is Deep learning in remote sensing applications: A meta-analysis and review Classification This section explores the different deep and machine learning (ML) techniques applied to common problems in satellite imagery analysis. Image formats, data management and catalogues. Note there is a huge volume of academic literature published on these topics, and this repo does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. random forests) are also discussed, as are classical image processing techniques. To a lesser extent classical Machine learning (ML, e.g. This document lists resources for performing deep learning (DL) on satellite imagery.
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