From Archived Historical Aerial Imagery to Informative Orthophotos: A Framework for Retrieving the Past in Long-Term Socioecological Research

Pinto, Ana Nóbrega; Gonçalves, J.; Beja, Pedro; Honrado, João P.

doi:10.3390/rs11111388

Cited by 38 publications

(39 citation statements)

References 55 publications

(103 reference statements)

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“…These error values were like those obtained by similar studies [52]. Similarly, in [53] 1947 historical images were scanned with a resolution of 21 microns to generate an orthomosaic with a mean error of 1.97 m and root mean square error (RMSE) of 1.00 m, with errors ranging between 0.90 m to 5.88 m. Table 5 presents MAD and NMAD (Equation (3)) values, as well as the absolute inter-percentile range at the 90% confidence interval (useful for an asymmetric error distribution). It is worth noting that the difference between the percentiles 5% and 95% (90% of confidence level) is ca.…”

Section: Suitability Assessmentsupporting

confidence: 90%

On the Use of Historical Flights for the Urban Growth Analysis of Cities Through Time: The Case Study of Avila (Spain)

et al. 2020

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Historical aerial images are a unique and relatively unexplored means of deriving spatio-temporal information for scenes and landscapes. Such historical imagery can be combined with photointerpretation and image-based 3D modelling techniques, providing the fourth dimension of time to 3D geometrical representations. This allows urban planners, historians, and other specialists to identify, describe, and analyse changes in scenes and landscapes. Urban growth has an important impact on the sustainable development of cities. An important step for the analysis of urban growth is the identification of different urban sectors. To this end, this paper proposes a methodology for the 4D urban growth analysis of cities through time using a free and open source software developed by the authors. This approach uses the latest advances in photogrammetry, including the so-called incremental Structure from Motion, to evaluate the urbanistic changes of a city by means of confronting two-point clouds from different eras. The objectives of this paper are twofold: (i) first, the processing of historical aerial images using modern photogrammetric techniques; (ii) second, deriving spatio-temporal information for urban cities, offering a method for researchers to identify changes over time. In order to validate this method, the urban growth of the city of Avila between 1956 and 2017 was assessed taking the historical American flight of 1956 and the digital aerial flight of 2017. The results were statistically assessed according to georeferencing quality, confirming that the approach developed can be used to support urban growth analysis through time and providing relevant data in 2D and 3D.

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Section: Suitability Assessmentsupporting

confidence: 90%

On the Use of Historical Flights for the Urban Growth Analysis of Cities Through Time: The Case Study of Avila (Spain)

et al. 2020

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“…In the remote sensing domain, historical aerial photographs were captured for various parts of the world starting in the early 1930s (Luman et al, 1997). Historical aerial black and white photographs have served as a key input for topics as different as for instance, landslide inventory and distribution analysis (Martha et al, 2012;Shu et al, 2019), vegetation dynamic studies (Carter et al, 2011;Hudak and Wessman, 1998;Jeter Jr and Carter, 2016;Kadmon and Harari-kremer, 1999;Lucas et al, 2010;Miller, 1999), landcover mapping (Caridade et al, 2008;Pinto et al, 2019;Robertson, 2012) and the evolution of river morphology (Abate et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fully convolutional networks for land cover classification from historical panchromatic aerial photographs

Mboga

Grippa

Georganos

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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Historical aerial photographs provide salient information on the historical state of the landscape. The exploitation of these archives is often limited by accessibility and the timeconsuming process of digitizing the analogue copies at a high resolution and processing them with a proper photogrammetric workflow. Furthermore, these data are characterised by limited spectral information since it occurs very often in a single band. Our work presents a first application of deep learning for the extraction of land cover from historical aerial panchromatic photographs of the African cities of Goma, Bukavu and Bujumbura. We evaluate the suitability of deep learning for land cover generation from a challenging dataset of photographs from the 1940s and 1950s that covers large geographical extents and is characterised by radiometric variations between dates and locations. A fully convolutional approach is investigated by considering two network architectures with different strategies of exploiting contextual information: one used atrous convolutional layers without downsampling, whereas the second network has both downsampling and learned upsampling convolutional layers (U-NET). The networks are trained to detect three main classes namely, buildings, high vegetation and a mixed class of bare land and low vegetation class. High overall accuracies of > 90% in Goma-Gisenyi and Bukavu, and >85 % in Bujumbura are obtained. This work provides a novel methodology that outperforms a baseline standard machine learning classifier for the exploitation of the vast archives of historical aerial photographs that can aid long-term environmental baseline studies. Future work will entail developing domain adaptation strategies in order to make the trained network robust for different image mosaics.

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“…GEOBIA has a diverse range of applications [4], including, for example, soil science [5], archaeological research [6], geomorphology [7], forestry [8][9][10], and agriculture [11]. Because of reduced spectral resolution, historical grayscale imagery is usually the object of visual interpretation techniques [12]. Individual [13,14] and sometimes even multiple classes [15] can be digitized on screen by GIS operators.…”

Section: Introductionmentioning

confidence: 99%

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

et al. 2020

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Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development.

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From Archived Historical Aerial Imagery to Informative Orthophotos: A Framework for Retrieving the Past in Long-Term Socioecological Research

Cited by 38 publications

References 55 publications

On the Use of Historical Flights for the Urban Growth Analysis of Cities Through Time: The Case Study of Avila (Spain)

On the Use of Historical Flights for the Urban Growth Analysis of Cities Through Time: The Case Study of Avila (Spain)

Fully convolutional networks for land cover classification from historical panchromatic aerial photographs

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

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