Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection

Hsu, Chia Yu; Li, Wenwen; Wang, Sizhe

doi:10.3390/rs13112116

Cited by 28 publications

(15 citation statements)

References 59 publications

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“…Other commonly acknowledged concerns include building GeoAI benchmark datasets, robust and explainable models, fusing and processing multi-source geospatial data sets, and enabling knowledge driven GeoAI research [17,437,441,442]. In recent years, increasing attention has been paid to ethical issues in GeoAI research.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

Hsu

2022

IJGI

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GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

Hsu

2022

IJGI

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“…The existing application is able to classify and detect lineament structure from Mars and Moon [17,26]. However, present deep-learning based methods are mainly focusing on crater detection [27][28][29], very few research are related with extraction of lineament structure on the Lunar surface based on deep learning networks due to the lack of such datasets.…”

Section: Related Workmentioning

confidence: 99%

A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5

Yan,

Liang,

Tian

et al. 2024

Sensors

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Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for the extraction of Lunar lineament structure. In this paper, a new lineament extraction method is proposed based on improved-UNet++ and YOLOv5. Firstly, new lineament dataset is created containing lineaments structure based on CCD data from LROC. At same time the residual blocks are replaced with the VGG blocks in the down sample part of the UNet++ with adding the attention block between each layer. Secondly, the improved-UNet++ and YOLO networks are trained to execute the object detection and semantic segmentation of lineament structure respectively. Finally, a polygon-match strategy is proposed to combine the results of object detection and semantic segmentation. The experiment result indicate that this new method has relatively better and more stable performance compared with current mainstream networks and the original UNet++ network in the instance segmentation of lineament structure. Additionally, the polygon-match strategy is able to perform preciser edge detail in the instance segmentation of lineament structure result.

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“…(2019), image processing and pattern techniques have also been applied for terrain analysis, but these algorithms tend to be poorly suited to terrain data, since they are generally not smoothly‐varying (Wang & Li, 2021). Machine learning has been recently introduced for feature detection with great success (e.g., Hsu et al., 2021; Steinfeld et al., 2013; Torres et al., 2020; Wang & Li, 2021; with true positive detection rates of about 60%–98%); data preparation for model training, though, can be time‐consuming and labor‐intensive since training data requires pre‐existing data sets that are generally developed using semi‐automated or fully manual methods.…”

Section: Introductionmentioning

confidence: 99%

Automated Earthwork Detection Using Topological Persistence

Lapides,

Grindstaff,

Nichols

2024

Water Resources Research

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For thousands of years, humans have altered the movement of water through construction of earthworks. These earthworks remain in landscapes, where they continue to alter hydrology, even where structures have long since been abandoned. Management of lands containing earthworks requires an understanding of how the earthworks impact hydrology and knowledge of where the structures are located in the landscape. Various methods for detecting topographic features exist in the literature, including a set of rule and threshold‐based techniques and machine learning methods. These tools are either labor‐intensive or require special pre‐processing or a priori assumptions about structures that limit generalizability. Here, we test a topological analysis tool called “persistence” to determine if it is useful for earthwork detection in rangelands. We found that persistence can be used to detect earthworks with 83% precision and 64% accuracy. Breached berms and berms with significant upslope sedimentation are most likely not to be detected using persistence. These results indicate that persistence can be useful for terrain analysis, and it has the potential to substantially reduce manual effort in feature detection by identifying regions where berms may be found.

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Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection

Cited by 28 publications

References 59 publications

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5

Automated Earthwork Detection Using Topological Persistence

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