Search Results (14)

Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve the automation of this vectorization process. Leveraging a recent benchmark for historical map vectorization, our method demonstrates increased robustness to disruptive factors and reduces the need for manual corrections. Full article

This work aims to map the optimal routes based on time and distance, via e-scooters and walking, to visit 54 historical heritage sites in Faro. Implementing these routes promotes environmental sustainability by reducing CO₂ emissions and encouraging healthier, greener tourism. The route optimization was conducted in ArcGIS, utilizing the Network Analyst extension and vector data obtained from OpenStreetMap. The results showed that there are routes that can be completed in one or more days, depending on visitors’ availability, physical capacity, or their chosen method of transportation. The optimal route to visit the 54 historical heritage sites forms a closed circuit spanning 17.35 km. If visits are split into two routes, one covering 31 monuments in the old city and the other 24 monuments in the exterior area of the urban center, the optimal closed-circuit routes measure 6.16 km and 11.31 km, respectively. This study is expected to enhance tourism promoted by the Faro municipality and make it more environmentally friendly. Full article

A WebGis tool called GoProsit has been developed to support winegrowers in planning a new sustainable vineyard and in the identification of high-quality terroir in Tuscany, Central Italy, by providing various information on soils, climate, hydrological risks, and fertilization. GoProsit, hosted by the web platform GEAPP, is a free, user-friendly, and interactive Geographic Decision Support System (GDSS). Soil data behind the WebGis tool has a 1 ha resolution, achieved by processing the legacy vector-type soil database of the Tuscany Region with the DSMART (Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees as supervised classification) algorithm, which disaggregated the map to 297,023 vineyard grid cells. Each grid cell holds climatic and pedologic information, along with physical and chemical features for each horizon of the most probable soil. GoProsit also provides soil maps in image format obtained by georeferencing about 50 historical soil maps (1969–2012). Finally, GoProsit runs and returns the outputs of six models: (a) carbon footprint, (b) potential erosion and maximum vine row length compatible with tolerable erosion, (c) potential water stress, (d) risk of runoff/waterlogging, (e) identification of suitable rootstocks, and (f) nutritional needs before planting. Statistics of the main model results for the investigated area are reported. This promising tool will soon be usable for the whole Italian territory; however, its potential makes it suitable for use in any wine-growing district. Full article

The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates. Full article

Historical geographic data play an important supporting role in the study of long-term geographic studies, such as climate change, urban expansion and land-use and land-cover change. These data vary in source, format and accuracy and are widely found in historical documents, old maps, produced vector data, aerial photographs, old photographs, etc. The complex nature of data makes it difficult for researchers to organize, store and manage in a unified manner. Thus, GIS practitioners and social scientists will collectively face the challenge of integrating historical data into spatial databases. Herein, we introduced the concept of a multi-level spatial grid, selecting Shanghai as the study area, to construct the Shanghai historical geographic database and give the conceptual model and processing method. The experiment was performed using the China Historical Geographic Information System (CHGIS), which showed the historical evolution of Shanghai more conveniently. Meanwhile, we simulated one million rows of historical geographic data in Shanghai and compared the retrieval efficiency of the encoding method with the latitude–longitude and geometric object indexing methods, which demonstrated that our method was very effective. This research is important for the construction of a historical urban database, which can better preserve historical resources and promote urban culture with information science and technology. Full article

This paper presents a holistic guiding methodology for the development of a geospatial tool to be used for the documentation, planning, smart management and dissemination of a country’s network of historic hiking trails. To deal with the challenges and to ensure the sustainability of a historic site, geospatial documentation merging authoritative and crowdsourced data and a WebGIS-based spatial analysis is necessary. Geospatial data collection should include professional field surveys, professional and crowdsourced photographic documentation and video recording of the existing historic walking/hiking trails. A geodatabase, structured using relational model technology, including vector spatial entities (feature classes), mosaics (raster) and tabulated data (geodatabase tables), should be developed on a commercial or open platform; in this case, the ArcGIS Pro is used. Entities with embedded descriptive information and metadata for the technical, legal, historical, and administrative context may then be created. An object-oriented data model is needed to connect spatial and descriptive information. Spatial and descriptive queries or correlations between attribute fields of spatial entities must be enabled for specialized information retrieval by either experts or users. Next, a web GIS application to present the developed geodatabase in a visually appealing and informative way is created. It should integrate 2D maps with built-in tools and should support advanced functionalities, such as: (i) pop-ups that display brief information and images about specific spots along the trails; (ii) dynamic visualization of the vertical profile of each trail; (iii) multimedia information about landmarks, natural features and scenic viewpoints. Finally, the tool includes a feedback service and continuous efficiency monitoring and assessment, and enables adjustments, if and where needed. The tool is tested and used for 10 historical walking/hiking trails of the archaeological and Holy Site of Meteora, Central Greece. This is a UNESCO World Heritage site. The network, with a total length of 35 km, leads to six monasteries, still active since the 12th century, passing by gigantic rocks and beautiful natural landscapes. The site is famous globally and the greater area is continuously overcrowded with visitors. The tool is anticipated to be used for the documentation and management of the whole walking/hiking historic trail network of Greece in the future. Full article

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management. Full article

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process. Full article

In this study, we aimed to further the international discussion on the methodology of applying GIS technology to the editing of large-scale cadastral maps, taking the experience of editing the cadastral map of Toruń from 1910–1915 as an example. We present the concept of building a georeferential spatial database of topographic–historical objects (GSDoT-HO), which includes the stages involved in creating the database, its exemplary structure, and a proposal of good practices in this process, which were developed in the course of previous projects using a geographic information system for Historical Atlases of Polish Towns. Our works included the scanning, calibration, and rectification of a total of 178 sheets of cadastral maps (including 154 sheets of the map of Toruń and 24 sheets of the cadastral map of the then-village of Mokre) at differentiated scales of 1:250, 1:500, 1:1000, and 1:2000. Finally, in the process of vectorization, vector and attribute data were acquired, which made up the final result in the form of GSDoT-HOs. This database was created out of seven information layers with linear or polygon geometries, including the two most important layers, i.e., plots and buildings, which for the then-area of the city of Toruń, contained approximately 5800 and 10,800 vectorised polygon objects, respectively. This article shifts the focus of the discussion of standards in the use of GIS technology to edit Historic Towns Atlases from the development of interactive maps to the construction of a database that should enable comparative studies of urban spaces. Full article

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures. Full article

Peri-urban areas constitute an enormous resource in terms of natural capital, landscape heritage and economic activities, but, at the same time, they are often affected by physical and socio-economic degradation, drawing the attention of decision makers and planners. Many studies have focused on these contexts both in terms of suburbs, with a close dependence on urban centers, and new land typologies. The present paper focuses on documentary evidence of the direct impacts of urban growth on rural lands. The study area entails the Vesuvius National Park, which, belonging the Naples metropolis, is well-known for its historical, geo-morphologic and naturalistic value. Furthermore, the area has a history of high-quality cartographic production: the 1817, 1907, 1960, 2009 time steps maps were digitized, georeferenced, vectorized and compared in a GIS environment. The results highlight a strong change in land-use, in vineyards and urban class types, with a more disaggregated landscape mosaic. The approach shows that the historical modeling of land-use changes supports the understanding of current land-use dynamics and landscape patterns. The study also shows the need to integrate landscape planning and landscape ecology approaches, highlighting the close interactions between urban, agricultural and natural areas, for the purpose of supporting decision makers in land-use management and conservation policies. Full article

The hydration potential of a landscape is an increasingly important attribute in a time of advancing climate change, making its assessment also a matter of some urgency. This study used the landscape ecological approach involving the hydrological balance, in which the soil water retention capacity (SWRC) and landscape water retention capacity (LWRC) are evaluated. To support our assessment of the water retention capacity in the landscape (LWRC), we used a synthetic interconnection of analytical vector layers of selected physical parameters of soil subtypes and secondary landscape structure (SLS) to create homogeneous polygons in the GIS Arc/Map10 computing environment. Selected abiotic and biotic attributes were assigned coefficients using a simple algorithm according to the authors, which were projected into landscape ecological complexes (LEC) in the GIS computer program in the Arc/Map10 program. We used hydrological balance calculations to specify the volumes of water retained in the landscape. The aim is to spatially estimate the retention capacity of the landscape, taking into account the current land use, including historical anti-erosion measures to reduce unwanted water runoff and soil erosion. Using zonal statistics, we achieved the following results. The part of the model area with very low or low LWCR represents 39.91% of the agricultural land used. We recorded a high LWCR on 17.69% of the area, with a predominance of meadows and cultizol cambis and cultizol fluvials. The calculation of the hydrological balance, which represents only 22.9% of atmospheric precipitation, also made a significant contribution to our knowledge of the LWRC. Full article

Introduction: Rio de Janeiro is the second-largest city in Brazil, with strong socio-spatial segregation, and diverse and heterogeneous land use, occupation, and landscapes. The complexity of dengue requires the construction of surveillance and control tools that take into account the historical, social, economic, and environmental processes mediated in the territory as a central axis of public policy. In this context, this study aimed to stratify the city into areas of receptivity to dengue, using innovative “territorial indicators” because they are built based on the actual occupation of the territory. Methods: We designed and constructed 17 indicators that sought to characterize the transformed and inhabited space according to receptivity to dengue. We used data on land use and occupation, connectivity, climate, and landscape. We developed the dengue receptivity through principal component analysis (PCA), using multiple criteria analysis and map algebra integrated in a GIS platform. Results: The most receptive areas were concentrated in the transition between the north and west zones of the city, a region of unconsolidated urban sprawl. The areas of greatest receptivity had the highest incidence and density of Aedes eggs during the study period. The correlation between receptivity index and incidence rate was positive in the epidemic years. Conclusion: The proposed set of indicators was able to identify areas of greater receptivity, such as regions of disorderly urban sprawl, with a concentration of social and environmental processes that are related to the occurrence of dengue outbreaks and high vector density. On the other hand, population immunity plays an important role in the spatial distribution of dengue during non-epidemic years. Full article

The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management. Full article