The study integrated Geographic Information Systems, remote sensing and the Analytic Hierarchy Process (AHP) to generate a groundwater-potential map. Six thematic layers—rainfall, drainage density, slope, soil type, geology, and land use/land cover—were selected based on their reported hydrogeological influence and the availability of data. After standardization and assignment of normalized weights, the researchers used weighted-overlay analysis in ArcGIS Pro 3.1.0 to combine the layers. The resulting map divided the study area into five categories: very low, low, medium, high and very high groundwater potential.

Kitgum’s mapped area was described as predominantly having very low to low potential, with the abstract identifying higher drainage density and steeper slopes as constraints. Pader showed a contrasting modeled pattern: 85.5% of its area was placed in the high or very high categories, which the researchers associated with gentler slopes and higher rainfall that they said enhanced infiltration capacity. These are characteristics identified by the model and its AHP weighting framework, rather than direct measurements of groundwater volume at every mapped location.

For validation, the researchers compared the model with yield data from 18 existing boreholes. They reported a 75.0% alignment between model classifications and actual well performance and described this as confirming the model’s reliability at an acceptable level. The study is presented as a preliminary GIS-based assessment for data-scarce environments, and its map is intended as a scientific basis for more targeted groundwater exploration. The available evidence comes from the abstract of an open-access, peer-reviewed article shared in an early version subject to further editing; it does not provide the full model inputs, weighting values, detailed borehole results or an uncertainty analysis. The validation sample of 18 boreholes and the preliminary nature of the assessment also limit the precision with which the mapped zones can be interpreted.

Subject of Research: Preliminary GIS and AHP assessment of groundwater potential in Kitgum and Pader districts, Uganda

Article Title: Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda

Article References: Dirie, A. A., Mohamed, A. A., & Kannapiran, A. (2026). Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda. Discover Sustainability. https://doi.org/10.1007/s43621-026-04943-7

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04943-7

Keywords: groundwater potential, Kitgum District, Pader District, Uganda, geographic information systems, remote sensing, analytical hierarchy process, weighted overlay analysis, borehole validation, water-resource mapping, Mapping, groundwater

The research team, led by scientists working within the framework of the Worldwide Bioclimatic Classification System developed by Salvador Rivas-Martínez, set out to resolve a persistent problem. Previous efforts to classify and name Mexican vegetation types, stretching back to the late nineteenth century, produced valuable but inconsistent results because they lacked shared criteria and joint input from both geographical and botanical perspectives. By combining climatic data with advanced geographic information system techniques, the researchers hoped to unravel the complex interactions between climate and vegetation and to provide a sound, unified system for identifying the country’s bioclimatic physiognomic patterns.

Bioclimatology, the discipline at the heart of the study, examines the relationship between long-term weather patterns and the responses of living organisms. Its intellectual roots reach back to pioneers such as Alexander von Humboldt and Carl Troll, who explored the links between climate and vegetation, and it matured as an independent field during the twentieth century as data collection improved and awareness of climate change grew. The Rivas-Martínez system used in this research is built on the close interrelationship between climate, vegetation and geography. It treats seasonality of precipitation, temperature and continentality, the difference between the warmest and coldest months, as the key variables that determine which bioclimates exist within broader macrobioclimates, and it establishes a predictive, hierarchical typology that nuances vegetation responses within the same macrobioclimate.

To build the bioclimatic side of the map, the team drew on the Digital Climatic Atlas of Mexico, which compiles monthly and annual averages of precipitation and temperature from 1902 to 2011 as recorded by the National Meteorological System. From these data they calculated a suite of bioclimatic parameters and indices, including the thermicity index, which weighs the intensity of cold as a limiting factor for plant communities, the compensated thermicity index, which corrects for excess cold or temperance in continental or hyperoceanic territories, and the annual ombrothermic index, the ratio of positive precipitation to positive temperature, which determines the ombrotype. The ombrothermic index of the driest bimonth captured the severity of seasonal drought, a decisive factor in a country where water availability varies dramatically from region to region.

The vegetation side of the analysis came from the National Institute of Statistics and Geography of Mexico, whose land cover data at a scale of 1:250,000 were regrouped according to the Standardized Hierarchical Vegetation Classification. The researchers reclassified the vector layers into three physiognomic categories: forests dominated by woody-stemmed plants over five metres tall, shrublands dominated by plants with woody or succulent stems mostly under five metres, and herbaceous and non-vascular formations dominated by plants without a woody base. Water bodies and cultural land cover types were also depicted. Working at a final scale of 1:4,000,000, polygons smaller than 256 square kilometres were merged with adjacent larger polygons, a pragmatic decision that simplified the mosaic while preserving the dominant patterns.

The map crossing procedure, performed in ArcMap GIS 10.5, revealed five ombrotypes and five thermotypes as the major bioclimatic classes of Mexico. The ombrotypes range from (Hyper)Arid through (Semi)Arid, Subhumid and Humid to Hyper-Ultra Humid, while the thermotypes span Infra, Thermo, Meso, Supra and Oro tropical temperature classes. Arid conditions dominate the national picture, covering 55.6 percent of the surface, with Subhumid and Humid zones together accounting for roughly 43 percent. Combining these classes yielded 13 distinct bioclimatic belts, the most widespread being the (Semi)Arid Mesotropical, which alone covers 33.5 percent of the country. The (Semi)Arid and Subhumid Thermotropical belts follow at 14.8 and 13.2 percent respectively.

Crossing these bioclimatic belts with the physiognomic data allowed the team to recognise 17 native vegetation types: 11 forests, 3 shrublands and 3 herbaceous formations. The forests tell a story of climatic gradients. Spiny deciduous woodlands locally known as Mezquital and Deciduous Dry Forests occupy more than 156,000 square kilometres of arid and dry Infra-Thermo-Meso-Supratropical terrain. Subhumid to Humid Thermotropical and Mesotropical forests, covering some 328,000 square kilometres, comprise Subdeciduous broad-leaved forests and needle-leaved conifer forests respectively. Supratropical forests cling to mountainous landscapes, marking transitions toward the Mexican timber line, while the rare Orotropical Hyper-Ultra humid belt supports evergreen perennial broad-leaved forest.

Shrublands emerge as the quiet giant of the map, covering 532,521 square kilometres and representing climates under water stress for long stretches of the year. This category encompasses the astonishing xerophytic shrublands of the Mexican deserts, including Cardonal dominated by the giant cactus Pachycereus pringlei, Tetetzal built on Neobuxbaumia tetetzo, Izotal with Yucca periculosa, Nopaleras of Opuntia cacti, and Magueyal of Agave species. A substantial portion, 158,117 square kilometres, corresponds to the Mediterranean macrobioclimate, supporting Chaparral shrublands dominated by genera such as Adenostoma, Arctostaphylos, Ceanothus and Quercus, found in Baja California and extending into Chihuahua. Herbaceous and non-vascular formations, meanwhile, spread across more than 165,000 square kilometres of arid to subhumid belts.

Perhaps the most sobering figure in the study concerns human influence. Cultural areas, reflecting agriculture, urbanisation and land-use change, now cover 529,794 square kilometres, or 27.3 percent of the national territory, and they predominate in the (Semi)Dry Meso-Supratropical regions. Because small patches of cropland were merged into neighbouring vegetation polygons during cartographic simplification, the authors warn that the true cultural footprint is underestimated, and they estimate that up to 51 percent of the country could ultimately be converted to cultural land cover. Water bodies, critical for aquatic biodiversity, account for a comparatively modest 12,958 square kilometres.

The implications reach well beyond cartography. Vegetation patterns serve as indicators of climate change impacts, guiding predictions and strategies for mitigation and adaptation, and they underpin ecosystem services from water purification and pollination to climate regulation. The authors note that regions experiencing arid conditions may face mounting challenges related to water scarcity and desertification, while shifts from Humid toward Subhumid ombrotypes have already been projected for Mexico. By providing a spatially explicit, climate-linked baseline of the country’s native vegetation, the new map offers conservation planners, land managers and policymakers a tool for identifying biodiversity hotspots, targeting restoration, and anticipating where climatic change will press hardest on ecosystems. The team acknowledges that finer-scale floristic data and soil information remain to be integrated, but the core message stands: harmonising bioclimatic and geobotanical methodologies is essential if megadiverse nations are to understand, and defend, the living landscapes that climate and history have given them.

Subject of Research: Bioclimatic and vegetation mapping of Mexico's plant formations

Article Title: A coupled cartographic approach between bioclimatology and vegetation formations of Mexico

Article References: Gopar-Merino, F., Velazquez, A., González-Pérez, A., del Río, S., Mas, J. F., & Penas, Á. (2024). A coupled cartographic approach between bioclimatology and vegetation formations of Mexico. Vegetation Classification and Survey, 5, 153-164. https://doi.org/10.3897/vcs.120442

Image Credits: AI Generated

DOI: 10.3897/VCS.120442

Keywords: bioclimatology, vegetation classification, Mexico, mapping, biodiversity, ombrotypes, thermotypes, shrublands, forests, GIS, conservation, climate change

The motivation behind COMPACT lies in a well-established body of research on situation awareness. Since Mica Endsley’s foundational work in the 1990s, scientists have understood situation awareness as a three-stage process: perceiving the relevant elements in an environment, comprehending what they mean, and projecting how they will evolve. A COP does not automatically deliver any of this. It provides an information space in which users can collect, filter, compare, and interpret operational data, and its value depends on how well the underlying system supports data fusion, deconfliction, and the inference of operational context. In multi-domain operations, where land, air, maritime, cyber, and space effects converge, the challenge of integrating heterogeneous data sources has only grown, producing a tangle of protocols such as NATO Friendly Force Information, Cursor on Target, and OTH Gold, and databases such as the Joint C3 Information Exchange Data Model.

Existing software covers important parts of this landscape, the authors argue, but none of it fills the specific niche COMPACT targets. The Team Awareness Kit ecosystem — ATAK on Android, WinTAK on Windows, iTAK on iOS, and TAK Server — excels at operational team awareness and distributed collaboration, but demands comparatively heavy deployment and administration. Humanitarian platforms such as Ushahidi and Sahana Eden support crisis mapping and resource coordination with different domain models. Geographic information system packages like ArcGIS and QGIS offer powerful spatial analysis, yet constructing a true operational picture requires tactical symbology, controlled scenario workflows, and integration mechanisms that generic GIS tools do not provide out of the box. Commercial command-and-control products such as Palantir Gotham or SitaWare are operationally mature but proprietary, blocking the source-level inspection and modification that reproducible science requires. Earlier academic prototypes, including tCOP, mCOP, and COPE, demonstrated the concept but were rarely released as reusable, browser-based, API-driven artifacts.

COMPACT positions itself precisely in that gap. It is not intended to replace mature operational C2 systems, specialized GIS platforms, or TAK-based field collaboration. Instead, it offers an open, modifiable environment in which researchers can construct, reuse, and vary COP scenarios under controlled conditions, while explicitly inspecting both the scenario model and the REST API that connects everything together. The contribution is deliberately architectural and methodological rather than algorithmic: the platform introduces no new data-fusion calculus or situation-awareness theory, but provides the substrate on which such research can be conducted repeatably.

Technically, COMPACT follows a modular client-server architecture. The backend is written in Java on the Spring Boot framework, while the frontend is a browser-based single-page application that offloads most processing to the client device, reducing server traffic. The codebase is organized into Maven submodules handling shared utilities, JPA-annotated domain models, decision-support services, business logic, and the web application. It ships as a ready-to-use artifact deployable in Docker, with an embedded H2 database for simple local installation and the option to migrate to PostgreSQL for persistent, multi-user deployments. Crucially, it can run in local, disconnected, or edge environments — a requirement for laboratories, training servers, and tactical network nodes where cloud connectivity cannot be assumed.

The platform’s unified scenario model is its central innovation. Military units, CBRN contamination reports, search-and-rescue assets, crisis-management incidents, points of interest, raster and vector layers, terrain data, and GIS objects are all represented in a single typed structure. Scenarios can be prepared through the graphical interface or generated programmatically through a Swagger-documented REST API supporting full create, read, update, and delete operations. This means instructors can prepare command-post exercises, staff-training sessions, or CBRN drills in which trainees analyze a shared picture and later revisit the identical scenario during debriefing. Researchers can generate comparable scenario variants, introduce controlled changes to information availability, and replay operational situations while external simulations or analytical models retrieve the current COP state through the API.

Beyond visualization, COMPACT embeds genuine geospatial analysis. Its decision-support services consume Digital Terrain Elevation Data files to compute flood-zone delimitations, terrain profiles along selected paths, and line-of-sight calculations for observation and reconnaissance planning, alongside distance, area, and coordinate-format tools. The platform ingests KML, GeoJSON, TopoJSON, and Mapbox Vector Tiles, and supports Web Map Service layers for weather and elevation. These are not merely cartographic conveniences: they let users evaluate how spatial conditions affect routes, exposure to hazards, accessibility, and the interpretation of reported events — the transition from a shared picture to task-oriented situation awareness.

The authors illustrate the platform with synthetic scenarios built around plausible operational workflows rather than any real incident. In a search-and-rescue scenario for a missing child, the coordinator integrates a last known position, witness reports, three SAR rings, four prioritized search sectors, and resources including a foot team, a K9 team, and a UAV thermal team, with terrain-profile and line-of-sight tools supporting route selection and drone observation planning. A second scenario tackles search under uncertainty: an unidentified aerial object whose signal degrades, forcing the operator to separate areas visible to ground teams from lower-confidence zones affected by vegetation and terrain masking, and to allocate UAV reconnaissance accordingly. A third demonstrates COMPACT as an embeddable widget in a multi-view headquarters dashboard, where separate COP views display different incidents, hazards, and resources while the REST API exchanges state with external analytical components.

Integration with external sources follows a deliberately explicit pattern. A simulator, sensor gateway, or experiment-control component publishes a position update to an external queue; a lightweight adapter validates the message, resolves the external object identifier to the corresponding COMPACT object, maps the fields to the correct data-transfer object, and submits the update through the REST API. The platform persists the change and the browser retrieves the modified state through the standard retrieval path. The scope is application-level interoperability based on JSON over HTTP — the authors are candid that semantic interoperability with operational C2 standards, native broker integration, and automated cross-source identity reconciliation remain future work, as do automated fusion mechanisms such as duplicate detection, conflict resolution, and confidence modeling.

The impact, the authors emphasize, is methodological rather than empirical. COMPACT does not claim that any particular COP design improves performance in all contexts; it provides a reusable experimental environment for studying how data availability, terrain analysis, interface configuration, and update mechanisms influence situation awareness, workload, decision accuracy, and coordination. No controlled evaluation with end users has yet been reported, and the platform should be understood as a research instrument rather than a validated decision-support system. But by releasing the code under an MIT license on a public repository, the team has lowered a barrier that has long kept common-operational-picture research confined to closed systems — and opened the shared map of modern operations to anyone with a browser and a question.

Subject of Research: An open-source software platform for constructing and studying common operational picture scenarios in multi-domain military and crisis-management research

Article Title: COMPACT: common operational multi-domain picture analysis combat & tactics

Article References: Frąszczak, D., & Chmielewski, M. (2026). COMPACT: common operational multi-domain picture analysis combat & tactics. SoftwareX, 36, Article 103106. https://doi.org/10.1016/j.softx.2026.103106

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103106

Keywords: common operational picture, situation awareness, open-source software, command and control, C4ISR, geographic information systems, REST API, search and rescue, terrain analysis, multi-domain operations, data fusion, crisis management

The original release of YOLO-MOD already offered something genuinely useful: a general-purpose framework for running YOLO object detection models inside standard QGIS workflows. Users could perform multi-class object detection using models in both PyTorch and ONNX formats, visualize the results as georeferenced vector layers overlaid on their maps, and export annotated datasets in the YOLO format for training future models. Basic post-processing utilities, such as layer merging, canvas tiling, and previewing exported annotations, rounded out an end-to-end workflow. But there was a significant constraint. The first version could only detect and classify five predefined object categories: ships, aircraft, helicopters, airports, and storage tanks. Anyone wanting to detect something else, from solar panels to wind turbines, was simply out of luck.

The updated version 2.1.1 removes that ceiling entirely. Users can now load and apply their own trained Ultralytics YOLO models, compatible with YOLOv8 and later architectures, with an arbitrary number and type of object classes. The technical mechanism behind this is elegantly simple. When a model stored in the PyTorch .pt format is loaded through the plugin’s interface, YOLO-MOD automatically extracts the object class names embedded in the model and dynamically generates the appropriate configuration controls in the user interface for each class. This means researchers can configure visualization and processing options for their custom classes exactly as they would for the built-in standard models, without ever touching a line of plugin code. The same workflow now applies to virtually any detection task based on horizontal bounding boxes.

The implications for applied remote sensing are considerable. A coastal monitoring team could train a model on harbor infrastructure and vessel types specific to their region, load it into QGIS, and immediately begin scanning aerial imagery for those objects. An airport safety analyst could build a detector for ground support equipment. An environmental agency could train a network to flag illegal dumping sites or deforestation edges. In each case, the results appear directly as color-coded bounding boxes on georeferenced layers, superimposed on basemaps such as Bing Aerial, meaning detections land in the same coordinate system as every other dataset in the project. That integration eliminates the clumsy export-import cycles that have long characterized deep learning workflows in the geospatial domain.

The update also tackles one of the most practical frustrations of working with automated detection: false positives. A new Edit Detections tab extends the plugin’s post-processing capabilities with interactive tools for removing individual detections from an existing vector layer. Users can select one or more detected objects, highlight them visually on the map, and permanently delete them without rerunning the entire detection process. This matters more than it might sound. In operational settings, analysts routinely spend hours cleaning up automated results before they can be used, and every detection run that must be repeated from scratch wastes computational resources and time. Being able to surgically remove spurious detections within QGIS itself keeps the analytical workflow inside a single environment.

The editing workflow is designed to feel native to QGIS users. Analysts select features using the familiar QGIS Selection Toolbar, for instance with the Select Features by Area or Single Click tool, and the plugin’s Selection Editing panel reports how many features are ready to be removed with a single button press. The layer merging functionality from the previous version has been retained and enhanced alongside these new tools, so refining a large multi-part detection result remains straightforward. The developers describe this as a way to let detection results be refined before further analysis or export, which is precisely the point in the pipeline where most real-world projects stall.

The third major enhancement concerns training data preparation, often the most labor-intensive phase of any machine learning project in remote sensing. The Canvas Tiling tool, which automatically splits the current QGIS map extent into image tiles suitable for training object detection models, has been upgraded with an interactive preview of the tile grid before any files are generated. Users configure tile width and height, the overlap percentage between adjacent tiles, the padding color for regions outside the available map content, and the output directory. Rather than committing immediately, a Preview and Start Tiling button first displays the proposed grid, letting the user verify the layout and then confirm or cancel the operation. Areas beyond the map content are automatically padded in the user-defined color, white by default, ensuring that every generated tile has identical dimensions regardless of the shape or size of the selected extent.

Under the hood, the plugin is built entirely in Python and designed for QGIS 3.x, with testing reported on versions 3.40 and 3.42 under Windows 11 using the embedded Python environment. Dependencies include PyTorch, with CUDA-enabled builds recommended for GPU acceleration, ONNX Runtime for flexible CPU and GPU inference, and NumPy. The software is distributed under the GNU General Public License version 3.0, versioned with Git, and publicly available through its GitHub repository, keeping it fully open source. That licensing choice matters for public agencies and research groups in developing regions, for whom commercial geospatial AI platforms can be prohibitively expensive and whose data policies sometimes forbid cloud-based processing altogether.

The broader context is a field moving fast. Object detection in remote sensing has been propelled by the growing availability of high-resolution imagery and by advances in detection algorithms since the original You Only Look Once architecture was introduced in 2016. Yet although several QGIS plugins now support selected deep learning tasks, the developers note that flexible frameworks for multi-category object detection remain relatively limited. YOLO-MOD’s approach of dynamically adapting its interface to whatever model the user supplies is a notable contribution to closing that gap, particularly for teams that lack the software engineering resources to build custom integrations.

What makes this release interesting beyond its immediate feature list is what it signals about the direction of geospatial software. The division between the GIS, where spatial data lives, and the machine learning environment, where models run, has been eroding for years, and tools like YOLO-MOD represent the pragmatic middle path: rather than rebuilding GIS capabilities inside a deep learning framework, they embed the models directly into the analyst’s existing environment. For the growing community of researchers tracking ships in shipping lanes, aircraft at airfields, and infrastructure across vast landscapes, the ability to load any YOLOv8 or later model, clean up its outputs interactively, and tile fresh training imagery from the same map canvas constitutes a genuinely streamlined loop, one that could shorten the cycle from data collection to working detector from weeks to days.

Subject of Research: A YOLO-based multi-category object detection plugin for optical remote sensing imagery integrated into the QGIS geographic information system.

Article Title: Version [ 2.1.1 ] – [ YOLO-MOD: YOLO-based multi-category object detection plugin for maritime, aviation, and infrastructure objects in optical remote sensing images in QGIS ]

Article References: Ciecholewski, M., & Strzelecki, M. (2026). Version [2.1.1]- [YOLO-MOD: YOLO-based multi-category object detection plugin for maritime, aviation, and infrastructure objects in optical remote sensing images in QGIS]. SoftwareX, 35, Article 103002. https://doi.org/10.1016/j.softx.2026.103002

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103002

Keywords: YOLO-MOD, object detection, QGIS, remote sensing, deep learning, YOLOv8, geographic information systems, Ultralytics, satellite imagery, maritime detection, infrastructure detection, open-source software