Rapid urbanization in India has accelerated the conversion of peri-urban agricultural land, posing significant challenges to sustainable land management, ecosystem services, and food security. This study investigates the spatio-temporal dynamics of agricultural land-use/land-cover (LULC) change and examines the socio-economic and environmental drivers of agricultural land loss in the peri-urban region of Rourkela, Odisha, during 2001–2021. Multi-temporal Landsat satellite imagery was analysed using remote sensing and GIS-based change detection techniques to quantify LULC transformations, while questionnaire surveys were used to assess local perceptions of the factors driving agricultural land conversion and its environmental implications. The results reveal a substantial decline in agricultural land by 40.77 km², accompanied by an increase of 41.04 km² in built-up area over the study period, indicating that urban expansion has occurred primarily at the expense of productive agricultural land. The spatial pattern of land transformation corresponds with rapid population growth, industrial development, and infrastructure expansion, whereas reduced agricultural profitability and climate variability were identified by respondents as major contributors to the decline of agricultural land. The integration of satellite-derived evidence with local perceptions provides a comprehensive understanding of peri-urban landscape transformation and its ecological significance. The findings highlight the need for sustainable urban planning and agricultural land conservation strategies to mitigate the adverse environmental consequences of uncontrolled urban growth and to enhance the resilience of peri-urban ecosystems and food security.
Traditional data sources, such as population censuses, have several limitations, including long update times, huge costs, and measurement bias, which can lead to gaps in understanding population dynamics. The aim of this article is to assess the quality of new technologies and the usefulness of the Web as a source of massive amount of geospatial data for mobility analysis. To this end, we would like to compare data obtained through web scraping with available population censuses to identify similarities or possible new trends. We will explore the travel routes offered on the carpooling site BlaBlaCar in Italy and compare them with data coming from the commuting matrix for study or work purposes elaborated by ISTAT(Istituto Nazionale di Statistica), a public research organization that produces and disseminates official statistics in Italy. Finally, we will use a gravity model by combining other open data on the web to try to estimate internal migration flows based on demographic and economic variables. While institutional data capture dense, routine commuting, BlaBlaCar data reflect longer, voluntary travel behaviours. Despite structural differences, both sources offer valuable insights into population movements.
This study aims to evaluate an automated approach for mapping areas affected by land use and land cover change using Sentinel-1 SAR imagery time series and machine learning models trained through a novel pseudo-labeling framework. The proposed framework automatically identifies reliable pseudo-labeled samples for training classifiers and represents an evolution of a previously proposed strategy developed for the same purpose. Different algorithms and attribute configurations were tested to automate the distinction between stable areas and regions subject to spatiotemporal changes. Two case studies were conducted in regions with distinct characteristics, located in Novo Progresso (Brazil) and Istanbul (Turkey), allowing a broad evaluation of the proposed method. The results indicate that the choice of classifier, the configuration of attributes, and regional characteristics influence the mapping performance. In Novo Progresso, the best results were obtained with SVM-Linear (kappa: 0.749; F1-Score: 0.842), followed by RF and RBFNet, while AdaBoost performed the worst. In Istanbul, RBFNet demonstrated higher accuracy (kappa: 0.759; F1-score: 0.852), confirming the effectiveness of the approach in detecting changes across different contexts.
Historical-territorial models propose spatial relationships among routes, nodes, terrain constraints and landmarks, but their geographical coherence is often assessed mainly through qualitative interpretation. This study develops a reproducible terrain-based GIS framework through an explicitly AI-assisted research process. Accessibility and landmark visibility are treated as two independent spatial tests. OpenAI's ChatGPT was used as an interactive methodological interlocutor to explore analytical alternatives, support the refinement of Python-based computational procedures and improve scientific clarity; all methodological choices, computational outputs, interpretations and conclusions were critically reviewed by the author. The terrain inputs, GIS settings and principal numerical results were independently checked by Dr Eleonora Maset of the University of Udine. The author retains responsibility for the study and its conclusions. The framework is applied to the Serbonis-Har Karkom corridor, proposed by Emmanuel Anati in the context of studies on a possible geographical reconstruction of the biblical Exodus. The historical interpretation is not tested here; the corridor is used as a spatially explicit model whose internal geographical organization can be evaluated independently. A NASA Shuttle Radar Topography Mission (SRTM) elevation model, with an effective working grid spacing of approximately 50 m in the study area, provides the common terrain basis. Functional accessibility is evaluated by comparing an unconstrained Least-Cost Path with a route constrained to pass sequentially through five predefined waypoints. The computation yields a reference-route length of 164.25 km and a constrained-route length of 188.79 km, corresponding to a 14.94
Dibru-Saikhowa National Park (DSNP), a dynamic riverine protected landscape within the Brahmaputra floodplain, is subject to persistent geomorphological and vegetation changes driven by fluvial processes. This study assesses long-term land-area dynamics and vegetation-cover changes in DSNP using Remote Sensing (RS) and Geographic Information System (GIS) techniques. A 1964 Survey of India (SOI) toposheet and Landsat imagery from 1994, 2004, 2014 and 2024 were used to analyse changes in park area, erosion, accretion and Normalised Difference Vegetation Index (NDVI)-based vegetation classes. The mapped park area decreased from 428.90 km² in 1964 to 291.72 km² in 2024, representing a net reduction of 137.18 km². Erosion exceeded accretion over the longer observation period, although during 2014–2024, erosion (44.04 km²) and accretion (43.23 km²) were nearly balanced. NDVI analysis revealed substantial temporal variation in vegetation condition: moderate vegetation cover declined from 197.86 km² to 98.92 km², while low vegetation cover increased to 183.70 km²; the high vegetation density class was detected in 2004 but was not detected after 2004. Analysis of initial vegetation condition and subsequent land persistence showed that 64.19
