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Dataset results
2 results for “Land Cover Segmentation”
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
Land Cover Aerial Imagery (LICAID) dataset for semantic segmentation
<p><strong>Dataset Highlights:</strong></p> <ul> <li><strong>Title:</strong> Land Cover Aerial Imagery Dataset (LICAID)</li> <li><strong>Focus Area:</strong> Franciacorta wine-growing region, Lombardy, Italy</li> <li><strong>Data Source:</strong> Satellite imagery from Google Earth Pro</li> <li><strong>Classes and Descriptions:</strong> <ol> <li><strong>Grasslands:</strong> Habitats dominated by grasses, with few or no trees, found in various climates from tropical to temperate regions.</li> <li><strong>Arable Land:</strong> Land predominantly used for growing crops.</li> <li><strong>Herb-dominated Habitats:</strong> Areas where non-woody plants (herbs) are the dominant vegetation, including meadows, prairies, marshes, and wetlands.</li> <li><strong>Hedgerows:</strong> Linear strips of vegetation consisting of shrubs, small trees, and grasses, often used to mark boundaries or provide wildlife habitat in agricultural landscapes.</li> <li><strong>Vineyards:</strong> Agricultural landscapes cultivated specifically for growing grapevines, typically for wine production.</li> <li><strong>Tree-dominated Man-made Habitats:</strong> Human-modified landscapes where trees are the predominant vegetation, such as urban parks, orchards, and landscaped gardens.</li> <li><strong>Olea europaea Groves:</strong> Groves or orchards of olive trees, primarily cultivated for the production of olives and olive oil, commonly found in Mediterranean regions.</li> </ol> </li> </ul> <p><strong>gy:</strong></p> <ol> <li> <p><strong>Data Acquisition:</strong></p> <ul> <li>18 orthophoto tiles manually selected from Franciacorta.</li> <li>Satellite imagery and corresponding shape files acquired from Google Earth Pro.</li> <li>Georeferencing of imagery using ArcGIS software.</li> </ul> </li> <li> <p><strong>Data Preparation:</strong></p> <ul> <li>Segmentation using multiresolution segmentation in eCognition software.</li> <li>Validation of segmented images by a plant expert using QGIS software.</li> <li>Manual annotation of seven land cover classes.</li> </ul> </li> </ol>
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