Use examples when they help make things clearer..

Deep learning techniques have been applied to various environmental applications, including land cover classification and ship detection using satellite imagery. These methods involve training a neural network model on large datasets of satellite or aerial images with corresponding labels to accurately classify different types of land cover or detect ships at arbitrary orientations. For example, U-Net + LSTM was used for crop type classification using Sentinel 1 & 2 data with higher resolution and more features produced better results. Mask R-CNN identified sports fields from multi-spectral remote sensing data overlaid on open-street-map. In terms of ship detection, Rotation-RetinaNet implementation on Optical and SAR ship dataset has been proposed to detect ships at arbitrary orientations using deep learning techniques. This project uses a rotated bounding box based CNN for ship detection in remote sensing images with Tensorflow object detection API. Other projects such as DAFNe, AProNet, AD-Toolbox, GGHL, and NPMMR-Det have also been proposed to improve the accuracy of oriented object detection using deep learning techniques on datasets like DOTA and HRSC2016. These methods involve anchor-free or rotated bounding box based proposals for ship detection with support for more datasets.

In terms of crop yield forecasting, researchers have developed methods for predicting crop yields based on satellite imagery data. By utilizing deep learning techniques to analyze the vegetation growth patterns in remote sensing images, these models can accurately estimate crop yields and help farmers optimize their management practices. For example, a recent study published in MDPI’s Remote Sensing journal used deep Gaussian processes for crop yield prediction based on satellite imagery data from the European Space Agency’s Copernicus program to accurately predict wheat yields across Europe. By utilizing this information, farmers can optimize crop management practices and ultimately improve crop yield.

In terms of ship detection, a recent project called “detecting-trucks” detected large vehicles in Sentinel-2 images using deep learning techniques similar to those used for aircraft recognition. This method involves training CNN models on satellite imagery data to accurately classify different types of vehicles and estimate their locations based on seasonal variations in vegetation growth patterns. By utilizing this information, farmers can optimize crop management practices and ultimately improve crop yield.

Overall, these deep learning techniques are being used in a variety of environmental applications to improve accuracy and efficiency in tasks such as crop type classification, ship detection, and aircraft recognition.