Automated terrain feature identification from remote sensing imagery: a deep learning approach

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment,...

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Bibliographic Details
Published in:	International journal of geographical information science : IJGIS 2020-04, Vol.34 (4), p.637-660
Main Authors:	Li, Wenwen, Hsu, Chia-Yu
Format:	Article
Language:	eng
Subjects:	Artificial neural networks Deep convolutional neural network (DCNN) Deep learning ensemble learning Environmental impact GeoAI Human populations Image detection Landforms Machine learning Neural networks object detection Object recognition Remote sensing scene interpretation Spatial data Terrain analysis
Online Access:	Get full text
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Summary:	Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model's characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.
ISSN:	1365-8816 1365-8824