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Applied Sciences
Special Issues
Geo-Processing of Historical Aerial Images
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Geo-Processing of Historical Aerial Images

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 2357

Special Issue Editors

Prof. Dr. Fabio Remondino

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Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca Cantoro

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Guest Editor
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
Dr. Thomas Dewez

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Guest Editor
BRGM, French Geological Survey, F-45060 Orléans, France
Interests: geodesy; surveying; remote sensing; geomorphology; historical images
Special Issues, Collections and Topics in MDPI journals
Dr. Elisa Mariarosaria Farella

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Guest Editor
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (2 papers)

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19 pages, 14244 KiB  
Article
Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching
by Adam Kostrzewa, Elisa Mariarosaria Farella, Luca Morelli, Wojciech Ostrowski, Fabio Remondino and Krzysztof Bakuła
Appl. Sci. 2024, 14(9), 3635; https://0-doi-org.brum.beds.ac.uk/10.3390/app14093635 - 25 Apr 2024
Viewed by 151
Abstract
In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block [...] Read more.
In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical. Full article
(This article belongs to the Special Issue Geo-Processing of Historical Aerial Images)
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14 pages, 7307 KiB  
Article
Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique
by Abdullah Harun Incekara, Ugur Alganci, Ozan Arslan and Dursun Zafer Seker
Appl. Sci. 2024, 14(4), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/app14041495 - 12 Feb 2024
Viewed by 693
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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