Geo-Processing of Historical Aerial Images

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 1777

Special Issue Editors

FBK-Bruno Kestler Foundation, 3DOM, Trento, Italy
Interests: photogrammetry; laser scanning; topography; mobile mapping; CH digitalization; 3D; AI
Special Issues, Collections and Topics in MDPI journals
Institute of Heritage Science (ISPC), Italian National Research Council (CNR), Area della Ricerca di Roma 1, 00010 Montelibretti, Italy
Interests: archaeology; 3D surveying; historical images
BRGM, French Geological Survey, F-45060 Orléans, France
Interests: geodesy; surveying; remote sensing; geomorphology; historical images
Special Issues, Collections and Topics in MDPI journals
3D Optical Metrology Unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38123 Trento, Italy
Interests: surveying; photogrammetry; 3D modeling; heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the availability of digitized historical aerial images has increased as many national archives or mapping agencies have started large scanning and archiving processes. Such historical datasets are a unique and quite unexplored means to map territorial changes and chronicle land-cover information over the past 100 years, with very high spatial and temporal resolutions. From these image datasets, 3D information can also be retrieved since many surveys were performed under photogrammetric (stereo) conditions. Long-term/multitemporal environmental monitoring, change analyses, or historical object detection can be based on processing these very rich time series of images.

The Special Issue aims to raise awareness and understanding of the recent activities related to digitization (i.e., scanning), processing, and dissemination of historical aerial images (among others). The Special Issue stems from the successful 2nd EuroSDR workshop on the geoprocessing and archiving of historical aerial images which was held in Rome in December 2022.

The Special Issue is seeking high-quality papers reporting progress, best practices, reviews, guidelines, and innovative solutions related to such rich patrimony. In particular, the following topics should be addressed in the proposed submissions: 

  • Best practices in scanning/digitization of historical aerial images;
  • Georeferencing processes;
  • Multitemporal analyses;
  • Change detection;
  • AI methods applied to historical images;
  • National scale processes;
  • Web access and sharing of digitized heritage contents.

Dr. Fabio Remondino
Dr. Gianluca Cantoro
Dr. Thomas Dewez
Dr. Elisa Mariarosaria Farella
Guest Editors

Manuscript Submission Information

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Keywords

  • historical aerial image
  • photogrammetry
  • digitization
  • 3D
  • orientation
  • DSM/DEM
  • AI
  • multitemporal analyses
  • change detection
  • heritage

Published Papers (1 paper)

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Research

14 pages, 7307 KiB  
Article
Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique
Appl. Sci. 2024, 14(4), 1495; https://doi.org/10.3390/app14041495 - 12 Feb 2024
Viewed by 408
Abstract
Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data [...] Read more.
Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year’s orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results. Full article
(This article belongs to the Special Issue Geo-Processing of Historical Aerial Images)
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