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Abstract GeoAI, or geospatial artificial intelligence (AI), has transformative potential for Earth science by integrating geospatial data with AI to enhance environmental monitoring, predictive modeling, and decision‐making. This commentary, based on the Greg Leptoukh Lecture at American Geophysical Union 2024, explores the evolving role of GeoAI in addressing pressing challenges—from environmental change in the Arctic to disaster response in hurricane‐prone tropical regions. It highlights advancements in GeoAI‐driven analysis of multimodal Earth observation data, ranging from structured remote sensing imagery to semi‐structured data, and natural language texts. The integration of knowledge graphs and generative AI further strengthens GeoAI by enabling seamless integration of cross‐domain data, semantic reasoning, and knowledge inference. By bridging informatics and domain expertise, GeoAI is shaping a more intelligent and actionable digital future for Earth science.
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Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection
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’ 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.
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- PAR ID:
- 10276644
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 11
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2116
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/rs13112116
