{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:06:37Z","timestamp":1784289997797,"version":"3.55.0"},"reference-count":77,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars\u2019 surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.<\/jats:p>","DOI":"10.3390\/rs13112116","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T03:45:29Z","timestamp":1622432729000},"page":"2116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection"],"prefix":"10.3390","volume":"13","author":[{"given":"Chia-Yu","family":"Hsu","sequence":"first","affiliation":[{"name":"School of Geographical Science and Urban Planning, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geographical Science and Urban Planning, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geographical Science and Urban Planning, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"ref_1","first-page":"433","article-title":"A review of Martian impact crater ejecta structures and their implications for target properties","volume":"384","author":"Barlow","year":"2005","journal-title":"Large Meteor. 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