Author ORCID Identifier
Matthew Haffner https://orcid.org/0000-0003-3060-3219
Papia F. Rozario https://orcid.org/0000-0002-2066-9479
Gustavo A. Ovando-Montejo https://orcid.org/0000-0002-4316-0528
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The results indicate that VGG16 and ResNet50 generally return more favorable results, and the tool appears to be an important first step toward building a more robust, multi-input, high resolution landscape search engine in the future. The tool, called LSE Wisconsin, is hosted publicly on ShinyApps.io
Haffner, M.; DeWitte, M.; Rozario, P.F.; Ovando-Montejo, G.A. A Neural-Network-Based Landscape Search Engine: LSE Wisconsin. Appl. Sci. 2023, 13, 9264. https://doi.org/10.3390/app13169264