Aerial Bombing Crater Identification: Exploitation of Precise Digital Terrain Models

Places of past conflicts and persistent objects that reflect such events often attract the attention of archaeological prospection which facilitates the construction of conflict narratives. Field prospection as a precise method for localization of aerial bombing craters (as an example of such persis...

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Bibliographic Details
Published in:ISPRS international journal of geo-information Vol. 9; no. 12; p. 713
Main Authors: Dolejš, Martin, Pacina, Jan, Veselý, Martin, Brétt, Dominik
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2020
Subjects:
aerial bombing
Airborne lasers
Algorithms
Archaeology
Artificial neural networks
Bombings
case studies
Conflicts
crater detection algorithm
crater localization
Craters
Detection
Exploitation
extraction
Extraterrestrial materials
Geomorphology
Identification
landscapes
Lasers
Lidar
Localization
Mask R-CNN
Methods
Neural networks
Object recognition
Pattern recognition
precision
readability
Resolution
spatial data
Terrain models
UXO
WWII
ISSN:2220-9964, 2220-9964
Online Access:Get full text
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Summary:Places of past conflicts and persistent objects that reflect such events often attract the attention of archaeological prospection which facilitates the construction of conflict narratives. Field prospection as a precise method for localization of aerial bombing craters (as an example of such persistent features) is a highly time- and resource-consuming task. Therefore, methods for automatic identification of such features are evolving. We present a comparison of three methods for possible automatic crater detection based on (a) extraterrestrial crater detection algorithms, (b) geomorphology-based edge extraction, and (c) image pattern recognition via a state-of-the-art convolutional neural network (CNN). All methods were preliminarily tested on a case study of eight Second World War (WWII) aerial bombing crater sites in NW Czechia via Airborne Laser Scanned LiDAR-derived digital terrain models with different spatial resolutions. We found that extraterrestrial crater detection algorithms and geomorphology-based edge extraction methods yield worse results given the standard indices of precision and recall. By comparison, the CNN method utilized for a particular task achieved satisfying results, predominantly with 0.5 m/px resolution (which is often available at the country level) of the input raster. Nevertheless, overall performance with this resolution varies significantly among the sites. Therefore, the quality and readability of the input data are crucial factors for the successful acquisition of precise ordinance location identification.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi9120713