Search Results (54)

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

This strategic document outlines Bulgaria’s roadmap for modernizing its cartographic sector from 2025 to 2035, addressing the outdated geospatial infrastructure, lack of standardized digital practices, lack of coordinated digital infrastructure, outdated standards, and fragmented data management systems. The strategy was developed in accordance with the national methodology for strategic planning and through preliminary consultations with key stakeholders, including research institutions, business organizations, and public institutions. It aims to build a human-centered, data-driven geospatial framework aligned with global standards such as ISO 19100 and the EU INSPIRE Directive. Core components include: (1) modernization of the national geodetic system, (2) adoption of remote sensing and AI technologies, (3) development of interactive, web-based geospatial platforms, and (4) implementation of quality assurance and certification standards. A SWOT analysis highlights key strengths—such as existing institutional expertise—and critical challenges, including outdated legislation and insufficient coordination. The strategy emphasizes the need for innovation, regulatory reform, inter-institutional collaboration, and sustained investment. It ultimately positions Bulgarian cartography as a strategic contributor to national sustainable development and digital transformation. Full article

Field-based landslide mapping is a crucial task for geo-hydrological risk assessment but is often limited by the lack of integrated tools to capture accurate spatial and temporal data. This research investigates a Direct Numerical Cartography (DNC) system’s ability to capture both spatial and temporal landslide features during fieldwork. DNC enables fully digital surveys, minimizing errors and delivering real-time, spatially accurate data to experts on site. We tested an integrated approach combining the Ground Operative System for GIS Input Remote-data Acquisition (GOGIRA) with the Expert-based Multitemporal AI Detector (ExMAD). GOGIRA is a low-cost system for efficient georeferenced data collection, while ExMAD uses AI and multitemporal Sentinel-2 imagery to detect landslide triggering times. Upgrades to GOGIRA’s hardware and algorithms were carried out to improve its mapping accuracy. Field tests in Western Greece compared data to 64 expert-confirmed landslides, with the Range-R device showing a mean spatial error of 50 m, outperforming the tripod-based UGO device at 82 m. Operational factors like line-of-sight obstructions and terrain complexity affected accuracy. ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. The combined DNC and AI-assisted remote sensing approach enhances landslide inventory precision and consistency while maintaining expert oversight, offering a scalable solution for landslide monitoring. Full article

Extreme weather events are increasing the frequency and intensity of forest fires, generating serious environmental and socio-economic impacts. These fires cause soil loss through erosion, organic matter depletion, increased surface runoff and the release of greenhouse gases, intensifying climate change. They also affect biodiversity, terrestrial and aquatic ecosystems, and soil quality. The assessment of forest fires by remote sensing, such as the use of the Normalised Difference Vegetation Index (NDVI), allows rapid analysis of damaged areas, monitoring of vegetation changes and the design of restoration strategies. On the other hand, models such as RUSLE are key tools for calculating soil erosion and planning conservation measures. A study of the impacts on soils and vegetation in the south of Salamanca, where one of the worst fires in the province took place in 2022, has been carried out using RUSLE and NDVI models, respectively. The study confirms that fires significantly affect soil properties, increase erosion and hinder vegetation recovery, highlighting the need for effective restoration strategies. It was observed that erosion intensifies after fires (the maximum rate of soil loss before is 1551.85 t/ha/year, while after it is 4899.42 t/ha/year) especially in areas with steeper slopes, which increases soil vulnerability, according to the RUSLE model. The NDVI showed a decrease in vegetation recovery in the most affected areas (with a maximum value of 0.3085 after the event and 0.4677 before), indicating a slow regeneration process. The generation of detailed cartographies is essential to identify critical areas and prioritise conservation actions. Furthermore, the study highlights the importance of implementing restoration measures, designing sustainable agricultural strategies and developing environmental policies focused on the mitigation of land degradation and the recovery of fire-affected ecosystems. Full article

The service-learning (SL) approach has shown effectiveness in fulfilling both academic and community-oriented objectives. This paper focuses on a specific case study for a Cartography, Remote Sensing, and Geographical Information Systems (GIS) course for Environmental Sciences undergraduate students. The main goals for implementing SL practice were (1) to enhance students’ GIS knowledge and to develop cross-cutting skills by working with real-world problems; (2) to share with society the knowledge acquired by students and ensure that it is valued; and (3) to prompt reflection on urban waste issues among students. The activity consisted of analyzing the waste containers along the 1 km riverbanks in León (Spain) and elaborating a proposal for the location of new rubbish bins to deliver to a City Council’s environmental technician. The results showed an improvement in students’ GIS management skills to solve environmental problems compared to those from the previous 3 years and a satisfactory response from environmental professionals with delivering the results. Together, an increase in students discussing urban waste was observed during the sessions. Projects like this not only teach technical skills but also provide a deeper understanding of data collection and implementation processes in environmental issues, which are closely aligned with professional experiences, and awareness of the practical application of the knowledge acquired. Full article

The analysis of land cover using deep learning techniques plays a pivotal role in understanding land use dynamics, which is crucial for land management, urban planning, and cartography. However, due to the complexity of remote sensing images, deep learning models face practical challenges in the preprocessing stage, such as incomplete extraction of large-scale geographic features, loss of fine details, and misalignment issues in image stitching. To address these issues, this paper introduces the Multi-Scale Modular Extraction Framework (MMS-EF) specifically designed to enhance deep learning models in remote sensing applications. The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. Extensive experiments were conducted across various deep learning models, and the framework was validated on two public datasets. The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets. Full article

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. Full article

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products. Full article

The EN280 Leba Road is a mountain road that runs along the western slope of Serra da Leba (Humpata Plateau) and its outstanding escarpments, connecting the hinterland areas of the Province of Huila to the coastal Atlantic Province of Namibe, in Southwest Angola. In the Serra da Leba ranges, as in Humpata Plateau, a volcano-sedimentary succession of Paleo-Mesoproterozoic age known as the Chela Group outcrops extensively. This main unit records a pile of sediments with a thickness over 600 m, overlying a cratonic basement with Eburnean and pre-Eburnean granitoids. This sequence is overlain in unconformity by the Leba Formation, which consists of weakly deformed cherty dolostones rich in stromatolites. Along the EN280 Leba Road, in the downward direction, were inventoried and characterized eight sites that, by their exceptional geological content and the singularity of their geoforms, are worth being defined and formalized as geosites: (1) traditional mining clay pit in the Humpata Plateau (post-Eburnean Paleo-Mesoproterozoic claystones); (2) old lime oven of Leba (post-Eburnean Meso-Neoproterozoic cherty dolostones with stromatolites); (3) viewpoint of the Serra da Leba (post-Eburnean Paleo-Mesoproterozoic volcano-sedimentary formations and Eburnean Paleoproterozoic granitoids); (4) vertical beds at the beginning of the descent (post-Eburnean Paleo-Mesoproterozoic volcano-sedimentary formations); (5) slope of the fault propagation fold (post-eburnean Paleo-Mesoproterozoic volcano-sedimentary formations); (6) reverse fault in granitoid rocks (Eburnean Paleoproterozoic granitoids); (7) Dolerite Curve (Eburnean Paleoproterozoic granitoids and dolerites); (8) ductile simple shear zone (Eburnean Paleoproterozoic granitoids and mylonites). These sites were primarily selected using the results of fieldwork (observations, measurements, reproduction of representations, and creation of models), interpretation of remote sensing data, and data from previously published bibliographies and cartography. A quantitative assessment of the selected sites to be preserved through their classification as geosites (integration in a geoconservation strategy) was proposed. The first position in the numerical assessment is occupied by the landscape dimension geosite “Viewpoint of the Serra da Leba”. This position is conferred, mainly, by its high geological, use, and Management values, being therefore considered the place with the highest geoheritage value in the studied area. Based on the previous characterization and evaluation, several field activities were proposed to be included in a guidebook, highlighting aspects such as landscapes, outcrops, rocks, structures, fossils, and georesources. The high scientific, didactic, and aesthetic values of these geological contexts and their high degree of geodiversity justify their integration into a geoeducational transect, contributing to the appreciation and awareness of the geological heritage of Serra da Leba, as well as to its promotion and scientific and educational dissemination. Full article

Transitional waters (TWs) are relevant ecological and economical ecosystems that include estuaries, deltas, bays, wetlands, marshes, coastal lakes, and coastal lagoons and play a central role in providing food, protecting coastal environments, and regulating nutrients. However, human activities such as industrialization, urbanization, tourism, and agriculture are threatening these ecosystems, which results in contamination and habitat degradation. Therefore, it is essential to evaluate contamination in TW to develop effective management and protection strategies. This study analyses the application of geospatial technologies (GTS) for monitoring and predicting contaminant distribution in TW. Cartography, interpolation, complex spatial methods, and remote sensing were applied to assess contamination profiles by heavy metals, and persistent organic compounds, and analyze contamination indices or some physicochemical water parameters. It is concluded that integrating environmental and demographic data with GTS would help to identify critical points of contamination and promote ecosystem resilience to ensure long-term health and human well-being. This review comprehensively analyzes the methods, indicators, and indices used to assess contamination in transitional waters in conjunction with GTS. It offers a valuable foundation for planning future research on pollution in these types of waters or other similar water bodies worldwide. Full article

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. Full article

During comprehensive regional prospecting evaluation and delineation of a prospecting target area, various types of data, including geological, geophysical, geochemical, and remote sensing, are usually integrated and visualized in a unified spatial environment, making it convenient for researchers to identify mineralization. To maximize the precision of spatial boundaries, the maps traditionally used in prospecting are predominantly in vector formats. However, with the rapid development of modern real-time 3D graphics and computer cartography technology, raster maps can now provide richer detail representation compared to traditional vector maps while still meeting the precision requirements. In this paper, we present a new GPU-based 3D visualization method for spatial data, specifically, two types of bitmap-based maps called dynamic geochemical maps (DGMs) and interactive geological maps (IGMs). A novel software system implementing this method was developed and has been applied in the exploration of the Zhunuo ore district, Tibet, showing large advantages over traditional vector maps. Full article

The halfway point for the implementation of the United Nations Sustainable Development Goals (SDGs) was marked in 2023, as set forth in the 2030 Agenda. Geospatial technologies have proven indispensable in assessing and tracking fundamental components of each of the 17 SDGs, including climatological and ecological trends, and changes and humanitarian crises and socio-economic impacts. However, gaps remain in the capacity for geospatial and related digital technologies, like AI, to provide a deeper, more comprehensive understanding of the complex and multi-factorial challenges delineated in the SDGs. Lack of progress toward these goals, and the immense implementation challenges that remain, call for inclusive and holistic approaches, coupled with transformative uses of digital technologies. This paper reviews transdisciplinary, holistic, and participatory approaches to address gaps in ethics and diversity in geospatial and related technologies and to meet the pressing need for bottom-up, community-driven initiatives. Small-scale, community-based initiatives are known to have a systemic and aggregate effect toward macro-economic and global environmental goals. Cybernetic systems thinking approaches are the conceptual framework investigated in this study, as these approaches suggest that a decentralized, polycentric system—for example, each community acting as one node in a larger, global system—has the resilience and capacity to create and sustain positive change, even if it is counter to top-down decisions and mechanisms. Thus, this paper will discuss how holistic systems thinking—societal, political, environmental, and economic choices considered in an interrelated context—may be central to building true resilience to climate change and creating sustainable development pathways. Traditional and Indigenous knowledge (IK) systems around the world hold holistic awareness of human-ecological interactions—practicable, reciprocal relationships developed over time as a cultural approach. This cultural holistic approach is also known as Systemic Literacy, which considers how systems function beyond “mechanical” aspects and include political, philosophical, psychological, emotional, relational, anthropological, and ecological dimensions. When Indigenous-led, these dimensions can be unified into participatory, community-centered conservation practices that support long-term human and environmental well-being. There is a growing recognition of the criticality of Indigenous leadership in sustainability practices, as well as that partnerships with Indigenous peoples and weaving knowledge systems, as a missing link to approaching global ecological crises. This review investigates the inequality in technological systems—the “digital divide” that further inhibits participation by communities and groups that retain knowledge of “place” and may offer the most transformative solutions. Following the review and synthesis, this study presents cybernetics as a bridge of understanding to Indigenous systems thinking. As non-Indigenous scholars, we hope that this study serves to foster informed, productive, and respectful dialogues so that the strength of diverse knowledges might offer whole-systems approaches to decision making that tackle wicked problems. Lastly, we discuss use cases of community-based processes and co-developed geospatial technologies, along with ethical considerations, as avenues toward enhancing equity and making advances in democratizing and decolonizing technology. Full article

The problem of the interferometric phase unwrapping in radar remote sensing of Earth systems is considered. Such interferograms are widely used in the problems of creating and updating maps of the relief of the Earth’s surface in geodesy, cartography, environmental monitoring, geological, hydrological and glaciological studies, and for monitoring transport communications. Modern radar systems have ultra-high spatial resolution and a wide band, which leads to the need to unwrap large interferograms from several tens of millions of elements. The implementation of calculations by these methods requires a processing time of several days. In this paper, an effective method for equalizing the inverse vortex field for phase unwrapping is proposed, which allows solving a problem with quasi-linear computational complexity depending on the interferogram size and the number of singular points on it. To implement the method, a parallel algorithm for solving the problem on a multi-core processor using OpenMP technology was developed. Numerical experiments on radar data models were carried out to investigate the effectiveness of the algorithm depending on the size of the source data, the density of singular points and the number of processor cores. Full article