With the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. For historical maps, this means an unprecedented scale of manual data entry and annotation. In this study, we first manually annotated the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.

Agricultural land cover and its changing extent are directly related to human activities, which have an adverse impact on the environment and ecosystems. The historical knowledge of crop production and its cultivation area is a key element. Such data provide a base for monitoring and mapping spatio-temporal changes in agricultural land cover/use, which is of great significance to examine its impacts on environmental systems. Historical maps and related data obtained from historical archives can be effectively used for reconstruction purposes through using sample data from ground observations, government inventories, or other historical sources. This study considered historical population and cropland survey data obtained from Ottoman Archives and cropland suitability map, accessibility, and geophysical attributes as ancillary data to estimate non-irrigated crop production and its corresponding cultivation area in the 1840s Bursa Region, Turkey. We used the regression analysis approach to estimate agricultural land area and grain production for the unknown data points in the study region. We provide the spatial distribution of production and its cultivation area based on the estimates of regression models. The reconstruction can be used in line with future historical research aiming to model landscape, climate, and ecosystems to assess the impact of human activities on the environmental systems in preindustrial times in the Bursa Region context.

The declassification of Cold War-era, US spy satellite imagery, collectively known as the Keyhole imagery, has contributed to hundreds of research projects across disciplines including geography, ecology, glaciology, archaeology, heritage studies, and urban studies. Corona (KH-4) and, increasingly, Hexagon (KH-9) imagery has garnered attention and excitement throughout the scientific community. The worldwide, repeat coverage and high resolution (up to 0.6m) of this imagery, which also has stereo coverage, is exceptional and not replicated elsewhere; it is also low-cost. However, declassified imagery has complex geometries and issues associated with processing and modeling. Even the initial imagery selection can deter adoption. This article addresses the critical issues involved with manipulating these declassified imagery. It synthesizes dozens of case studies across scientific domains to draw out commonalities in experiences at the selection, processing, and modeling stages. Issues with the image scans and ordering process, over- and underavailability of imagery, mosaicking, lack of metadata, ground control points, rigorous modeling, and image size are outlined. Approaches help guide researchers towards tailored solutions. Approaches to efficiently and effectively sort through imagery archives and georeferencing software options, and data management strategies are discussed. Two open-access tools for modeling, Sunspot and Ames Stereo Pipeline, are highlighted. The aim throughout is to increase the use of declassified historical imagery by orienting researchers to issues they can anticipate and approaches they can employ. The article represents the most comprehensive discussion of methods associated with declassified imagery published to date, that will enable accessibility and innovation.

This featured graphic visualises internal migration flows in Bulgaria using settlement-level census data from 1934 to 1946, highlighting how topography and local factors shaped migration and urbanisation. By combining spatial interaction models (SIMs) and edge-path bundling techniques, we reveal fine-scale migration corridors and the role of mountainous terrain in influencing mobility. The SIM approach goes beyond aggregate district analysis to provide new insights into rural depopulation and urban growth.

Bulgaria experienced dramatic rural decline alongside rapid urban growth during the 20th century, shaped by both demographic pressures and socioeconomic change. Today, it remains one of Europe’s fastest-declining populations, underlining the importance of understanding long-term migration dynamics. Understanding these migration dynamics is essential for interpreting the country’s broader population shifts. This study provides a spatial analysis of internal migration in Bulgaria from 1934 to 1992. We construct a harmonised geocoded census settlement dataset, combining historical population records with geospatial settlement boundaries, road network data, and terrain ruggedness measures. Distances between settlements are calculated using both Euclidean and road-network measures, and terrain effects are quantified through terrain ruggedness indices. Migration flows are estimated using spatial interaction models (SIMs), parameterised by population scaling and distance decay functions. Model outputs are validated against historical benchmarks and aggregated regional flows, as well as on the settlement level, by intercensal period variability, ensuring robustness between the intercensal periods. Our analysis investigates the role of challenging topography in shaping migration flows, showing how mountainous landscapes constrained movement while facilitating concentrated urban growth. By integrating historical census records with spatial modelling and geospatial analysis, we uncover local migration dynamics that remain invisible at larger scales. Although our study does not offer direct policy advice, it provides a quantified geospatial perspective on historical context for contemporary policy debates and urban planning initiatives in a country that has experienced both significant rural decline and rapid urbanisation. The findings shed new light on Bulgaria’s population history and provide a framework for understanding the interplay between landscape features and migration dynamics.

Accurate reconstruction of historical population distributions from the 1970s to the 1990s remains a significant limitation in global gridded population products due to coarse built-up data and limited census records. This study is, to our knowledge, the first to integrate declassified Hexagon KH-9 reconnaissance imagery into gridded population mapping. We enhance the GHS-POP framework by combining segmented built-up land cover from the HexaLCSeg dataset, derived from 1977 KH-9 imagery, with geocoded settlement-level census data to construct high-resolution historical population grids. Applied to Arnavutkoy and Cekmekoy in Istanbul for the period 1975-1990, we evaluate three dasymetric approaches, including a standard GHSL baseline, a Hexagon-enhanced workflow, and a fully integrated model incorporating local census records. Pixel-wise and zonal analyses show that GHSL misallocates populations to historically undeveloped regions, while the Hexagon-derived dataset substantially improves the representation of fragmented rural and peri-urban areas often missing from global products. Incorporating settlement-level LAU-2 census data further refines spatial population distribution. The results demonstrate that combining historical reconnaissance imagery with high-resolution census data improves the accuracy of historical population grids, and given the global coverage of declassified missions, this methodology offers significant potential for reconstructing historical population patterns in data-scarce regions worldwide.