Annotation of datasets for deep learning applied to satellite and aerial imagery - satellite-image-deep-learning/annotation
Deep 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 and environmental...
Deep Learning-Based Classification of Hyperspectral Data A U-net based on Tensorflow for objection detection (or segmentation) of satellite images - DSTL dataset but python 2.7 What’s growing there? Using eo-learn and fastai to identify crops from multi-spectral remote sensing data (Sentinel 2)...
The key obstacles in adopting Deep Learning techniques for glacial lake extraction are (i) maintaining the spatial features of the data during the encoding procedure (ii) the requirement of the proper and large dataset for training, and maintaining accuracy in lake detection and segmentation. ...
We will use Classify Objects Using Deep Learning for inferencing the results. The parameters required to run the function are: in_raster:The input raster dataset to classify. The input can be a single raster or multiple rasters in a mosaic dataset, an image service, or a folder of ...
The result is an automated deep learning framework that can produce highly accurate LULC maps images significantly faster than current semi-automated methods. The contribution of this article includes extensive experimentation of different FCN architectures with extensions on a unique dataset, high ...
Its ability in image classification, object detection and feature extraction has been frequently praised. However, it may also apply for falsifying geospatial data. To demonstrate the thrilling power of AI, this research explored the potentials of deep learning algorithms in capturing geographic ...
ROOF TYPE SELECTION BASED ON PATCH-BASED CLASSIFICATION USING DEEP LEARNING FOR HIGH RESOLUTION SATELLITE IMAGERY 3D building reconstruction from remote sensing image data from satellites is still an active research topic and very valuable for 3D city modelling. The roof model is the most important ...
https://www.kaggle.com/c/airbus-ship-detection/overview Rating - medium, most solutions using deep-learning, many kernels, good example kernel. I believe there was a problem with this dataset, which led to many complaints that the competition was ruined....
DSAMNet -> Code for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”. The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d)...