Aerial Bombing Crater Identification
Exploitation of Precise Digital Terrain Models
Bibliographic Data
| ID | 22033470 |
|---|---|
| Authors | Martin Dolejš (0000-0002-7821-897X, Jan Evangelista Purkyně University in Ústí nad Labem, corresponding author), Jan Pacina (0000-0002-7241-0274, Jan Evangelista Purkyně University in Ústí nad Labem), Martin Veselý (0000-0002-3147-4941, Jan Evangelista Purkyně University in Ústí nad Labem), Dominik Brétt (0000-0001-8045-4237, Jan Evangelista Purkyně University in Ústí nad Labem) |
| Year | 2020 |
| Volume | 9 |
| Issue | 12 |
| Pages | 713 |
| Publication date | 2020-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9120713 |
| OpenAlex | W3106605108 |
| Language | EN |
| Citations received | 8 |
| References cited | 29 |
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
Cartography · Computer vision · Digital elevation model · Geography · Impact crater · Orthophoto · Remote sensing · Terrain · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Computer Science · Image Processing and 3D Reconstruction · Artificial Intelligence · Geology
A GIS-based multi-criteria geospatial workflow for mapping the hazard, exposure and risk of explosive remnants of war in twentieth-century conflict landscapes
The Archaeology of Unexploded World War II Bomb Sites in the Koźle Basin, Southern Poland
Prospecting of Architectural Features Using LiDAR‐UAV Technology, Deep Neural Networks and Visualization Techniques
Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning Yolov4 framework
A multi‐temporal satellite‐based risk analysis of archaeological sites in Qazvin plain (Iran)
Evaluating Mask R‐CNN models to extract terracing across oceanic high islands
Deep learning reveals extent of Archaic Native American shell-ring building practices
Moated site object detection using time series satellite imagery and an improved deep learning model in northeast Thailand
Dynamics of Sediments in Reservoir Inflows
Detecting World War II bombing relics in markedly transformed landscapes (city of Most, Czechia)
Learning to Look at LiDAR
Object‐based image analysis
The application of LiDAR-based DEMs on WWII conflict sites in the Netherlands
Using deep neural networks on airborne laser scanning data
Object‐based Shell Craters Classification from LiDAR‐derived Sky‐view Factor
A witness in the landscape
Convolutional neural networks for archaeological site detection – Finding “princely” tombs
Second World War bomb craters and the archaeology of Allied air attacks in the forests of the Normandie-Maine National Park, NW France
Second World War conflict archaeology in the forests of north-west Europe
| Unique citing works | 8 |
|---|---|
| Citations per year | 1,6 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |