A Neural-Network-Based Landscape Search Engine: LSE Wisconsin
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing in Agriculture · Remote Sensing and LiDAR Applications · Species Distribution and Climate Change
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geography and Anthropology, University of Wisconsin–Eau Claire, Eau Claire, WI 54701, USA
Department of Environment and Society, Utah State University, Blanding, UT 84511, USA
Funding
Office of Research and Sponsored Programs at the University of Wisconsin-Eau Claire
References (28)
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