Climate-resilient urban planning with KLIMASCANNER: an AI-powered QGIS plugin

Introducing NLCD-Imp: A QGIS plugin to better replicate urban characteristics in land use/cover maps for SWAT

The article examines the hunting strategies employed by Neanderthals at Chagyrskaya Cave (Altai, Russia) through a comprehensive taphonomic and zooarchaeological analysis of Bison priscus remains from Upper Pleistocene deposits. The research focuses on prey selection — specifically, the age and sex composition of hunted individuals and the preferential transportation of specific carcass segments to the cave. Taphonomic evidence confirms that the majority of bison bones in layers 6a, 6b, and 6c result from anthropogenic accumulation. Zooarchaeological data reveal the butchery of at least 18 bison, with a predominance of prime-aged adults (3–6 years). The presence of adult individuals and the predominance of females suggest that Neanderthals primarily targeted matriarchal (mixed) bison herds typical of winter-spring, when post-rut herds are dominated by females, juveniles, and a limited number of males. Among the hunted bison, at least two were 3–4 months pregnant, which indicates an episode of hunting in winter and extends evidence for multi-seasonal cave occupation. If Bison priscus exhibited seasonal altitudinal migrations Neanderthals may have strategically occupied Chagyrskaya Cave to intercept migratory herds, suggesting a seasonal or follow-the-herd hunting mobility pattern. Estimations of minimal meat yield, calibrated against the approximate caloric requirements of a Neanderthal group (~15 individuals), suggest that it could sustain the group for approximately 70 days, excluding contributions from fat. Quantitative spatial analysis of skeletal elements, conducted via QGIS plug-in de­monstrates a selective transport bias toward meat-rich long bones, with diaphyseal fragments dominating the assemblage. The lack of epiphyses likely reflects anthropogenic processing or carnivore scavenging.

Ecosystem condition can be understood as the quality of an ecosystem in terms of its abiotic, biotic, and landscape characteristics. It is a measure of structural integrity, functional capacity, and resilience of any given ecological system. Its assessment is essential to support environmental objectives (e.g., nature restoration or sustainable use). Spatially explicit assessment of ecosystem condition requires integrating diverse geospatial data. Here, we present the EcoCondition Toolset, a QGIS plugin implementing a user-friendly GIS weighted-sum methodology for ecosystem condition assessments. It simplifies data preparation and analysis through five sequential toolsets: i) layer alignment and resampling; ii) no-data handling; iii) multicollinearity testing; iv) indicator normalisation and inversion; and v) condition assessment. The plugin calculates six specific ecosystem attribute – or state - composites (Physical, Chemical, Compositional, Structural, Functional, Landscape) from user-selected variables (in raster format), according to the System of Environmental-Economic Accounting. After data preparation and verification, the tool displays default equal weights for each composite and related variables, which users can adjust (e.g., to reflect stakeholder preferences).The toolset automates best-practice multicollinearity screening, normalisation, and flexible weighting for ecosystem condition assessment and monitoring.The resulting index preserves true severity and variation among ecosystem states.The results can support robust policy instruments and land-use decision-making, prioritising conservation and restoration actions.

Abstract. The lack of open and free tools for cartographic generalisation restricts the use of generalization techniques to National Mapping Agencies that can afford the development of custom processes based on software such as ArcGIS. For the others, whether they are students, researchers, independent cartographers or data journalists, the release of the version 1.0 of the CartAGen library can be a solution. CartAGen can be seen as a three-in-one tool. It provides first an open Python library that is complementary to Shapely and GeoPandas libraries to build automated generalisation scripts. Then, CartAGen is now (2) a QGIS plugin that can be used to generalise QGIS layers with many different algorithms that can also be included in a model builder. Finally, we provide (3) several Python notebooks that can be used as tutorials to discover the challenges of map generalisation, and how the library can be used. A significant effort has been made to provide documentation that is aimed at both novice and trained cartographers.

This article presents the ADAImpact tool, a QGIS plugin designed to assess the potential impacts of geohazards—such as landslides, subsidence, and sinkholes—using open-access surface displacement data from the European Ground Motion Service (EGMS), which is based on Sentinel-1 satellite observations. Created as part of the European RASTOOL project, ADAImpact integrates InSAR-derived ground movement data with exposure datasets (including population, infrastructure, and buildings) to support civil protection agencies in conducting risk assessments and planning emergency responses. The tool combines “Process Magnitude”, with “Exposure” metrics, quantifying the population and critical infrastructure affected, to generate potential impact maps for ground motion hazards. When applied to case studies along the Portugal–Spain border and the coastal region of Granada, Spain, ADAImpact successfully identified areas of high potential impact. These results underscore the tool’s utility in pre- and post-disaster assessment, highlighting its potential for scalability across Europe.

Cave-PY is a QGIS plugin developed to identify and analyze cave levels from geospatially referenced cave survey data. Cave levels, cave tiers, or cave stories are subhorizontal passages in karst systems that develop at different elevations due to base level changes or litho-structural factors. This algorithm processes point cloud data by calculating horizontal distances between survey points based on user-defined slope thresholds and proximity radius parameters. The horizontal extent is grouped into elevation classes to identify potential cave levels. We use Stortuvhola cave located in Northern Norway, a multi-level system, where we demonstrate the plugin's ability to effectively reveal cave levels from both survey station data and complete cave survey datasets. The sensitivity tests we performed highlight the importance of appropriate parameter selection based on survey characteristics. While Cave-PY offers an efficient method for the initial extraction of cave levels, it is important that the results are validated through morphological criteria and cave survey information for correct interpretation. We believe this tool addresses a gap in the existing methodology for geospatial analysis of caves.

HighlightsWhat are the main findings?A semi-automated method was developed for detecting maize crop damage using UAV-acquired RGB imagery, fully integrated within the QGIS environment.The method uses vegetation indices (ExG, GLI, MGRVI) and unsupervised k-means clustering, with interactive result tuning via a dedicated QGIS plugin.What is the implication of the main finding?The proposed approach enables fast, repeatable, and low-cost wildlife damage assessments without the need for multispectral sensors or artificial intelligence.The method can be operationally used by non-specialists without GIS or coding skills, making it ideal for farmers, field technicians, and local environmental managers.Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management.

Abstract. Mountain glaciers are highly sensitive to climate change, having lost nearly half their surface area and two-thirds of their volume in the European Alps since 1850. Recent advances in unmanned aerial vehicles (UAVs) and aerial imagery, combined with traditional in-situ Ground Control Points (GCPs) measurements, have enabled repeated data collection for glacier monitoring. However, analyzing and visualizing glacier changes remains a time-consuming process in Geographic Information System (GIS) environments. QGIS, an open-source GIS, supports custom plugins that automate routine tasks, improving accessibility and collaboration in climate research. Existing plugins facilitate environmental monitoring, hydrology, and remote sensing applications, streamlining spatial analysis without requiring programming expertise. Despite these tools, an integrated, user-friendly solution for glacier monitoring is still lacking. In this paper the GlacioTools QGIS plugin is presented. It is designed to simplify geospatial data processing for glacier studies. It automates in-situ survey reporting, generates surface displacement and velocity maps, and organizes GCP documentation within a single workflow. By enhancing efficiency and accessibility, GlacioTools supports researchers in documenting and analyzing glacier evolution more effectively.

Abstract. Palaeogeography is the study of the geography in the geological past, focusing on reconstructing the position of continents, oceans and mountain ranges over millions of years, helping scientists to understand past climates, the evolution of life and quantify sea-level variations. Plate tectonic models are essential for reconstructing palaeogeography, as they provide information about the position and age of geological features controlling the topography. The PANALESIS model, for instance, can be used to create fully quantified palaeogeographic reconstructions and sea-level variations estimates. However, the data and code used to produce previous results using PANALESIS were never published, were dependent on proprietary software, and can no longer be run due to software obsolescence, making them impossible to reproduce. To address this, we have entirely rewritten and enhanced the source code into a QGIS plugin named TopoChronia. In this paper, we present sea-level curves derived from the new palaeogeographic maps over the Phanerozoic, and compare them with the original PANALESIS sea-level curve as well as other data obtained with sequential stratigraphic studies. We discuss possible causes explaining differences in results. The TopoChronia plugin is available at https://github.com/florianfranz/topo_chronia

ABSTRACT In recent years, integrating geoprivacy functions into GIS applications has become crucial due to increasing data breaches and the need to comply with international guidelines. This paper introduces the MapSafe geoprivacy plugin, a comprehensive tool that allows scientists and practitioners to safeguard and verify sensitive geospatial data directly within their desktop GIS applications, where most geospatial data workflows – from creation to analysis – take place, without relying on centralised third parties. The QGIS plugin enables data owners to share anonymised datasets with users having lesser privileges using donut masking or hexagonal binning, securely protect the original dataset with robust encryption and public-key passphrase protection, and notarise the encrypted file’s details on the blockchain, allowing recipients to verify its integrity before decrypting and displaying. MapSafe expands upon its browser-based predecessor, bringing these capabilities to desktop GIS applications, handling larger datasets, and empowering individuals and organisations to meet data protection regulations with ease. The user interface prioritises usability, with clear documentation, tooltips, and video guide included to educate about the features. Our aim is for this desktop plugin to make geoprivacy more accessible to users with different GIS backgrounds.

<p>Among all other natural resources, droughts are progressively affecting our communities because to their gradual and sustained evolution over several years. Droughts impact agriculture and result in catastrophic consequences. The present research sought to model the spatial variability and occurrence of drought in Babylon city and its vicinity utilizing GIS and remote sensing through a subjective model, specifically the Analytical Hierarchy Process (AHP), integrated with Geographical Information System (GIS) and various influencing parameters. In this study, 12 parameters, namely elevation, slope(degree), LCLU, land surface temperature (LST), normalized difference moisture index (NDMI), normalized difference building index (NDBI) and soil moisture index (SMI), Annual rainfall (mm), Evaporation, Relative humidity, and SPI. were used to delineate the drought in study area. We employed the QGIS plugin to analyses datasets and evaluate drought prevalence using MCA. The plugin incorporates GIS-based appropriateness analysis and multi-criteria decision analysis, mitigating financial and technological obstacles for analysts and planners in poor countries. The MCDA model's peak performance in the "ideal location" scenario, with a (kappa value = 1), demonstrates its robustness and trustworthiness in identifying drought conditions in the Babylon area. the study classified area into four levels/zones of drought prevalence: mild, moderate, severe, and extreme. The region is highly susceptible to drought, with 55.4% experiencing extreme drought, 16.1% experiencing mild drought, and 3.3% experiencing no drought. Water deficits vary across areas, and 59.57% of the study area falls within the high drought zone.<em> </em></p>

This study explores how urban morphological and environmental factors influence Urban Heat Islands (UHIs) using a geospatial modeling approach. The aim of the research is to develop a methodology to assess UHI effects, emphasizing the role of urban morphology, land use, and vegetation in nighttime heat accumulation. A micro-scale analysis with a 50 m resolution is conducted by integrating a custom QGIS plugin with open-access data, ensuring broad applicability. The 50 m resolution was chosen because it allows for the capture of local variations in UHI intensity while maintaining the scalability of the urban analysis across different city contexts. Non-parametric statistical analyses (ANOVA, Kruskal–Wallis H test, and correlation assessments) were used to evaluate the relationships between the urban parameters—wind corridors, altitude, vegetation (NDVI), surface water (NDWI), and the Sky View Factor (SVF)—and Nighttime Land Surface Temperature (LST). Given that UHI variations during summer, particularly in cities of the Iberian Peninsula, are closely linked to summer heat severity, this factor was considered to classify the cities for the study. Correlation analyses confirm that all tested factors influence LST, with wind corridors being the least significant. The model performance evaluation shows the highest errors in cities with lower summer severity (RMSE = 1.586 °C, MAE = 1.2686 °C, MAPE = 6.99%) and the best performance in warmer cities (RMSE = 1.4 °C, MAE = 1.14 °C, MAPE = 4.5%). Validation in four cities of the Iberian Peninsula confirmed the model’s reliability, with the worst RMSE value of 2.04 °C. These findings contribute to a better understanding of the factors driving UHIs and provide a scalable assessment framework.

The Coastal Dynamics Analyzer (CDA) v.2.0 is an enhanced version of the existing QGIS plugin, CDA v.1.0, which was originally developed for transect-based analysis (TBA) in coastal erosion assessment. This new release introduces the Area-Based Analysis (ABA), a new methodology useful for analyzing irregular or dynamic shorelines. By integrating ABA, CDA v.2.0 enables users to evaluate broader coastal dynamics and changes, providing a more comprehensive toolkit for researchers and professionals engaged in coastal monitoring and management. The plugin maintains its user-friendly interface and compatibility with QGIS, ensuring seamless integration into existing workflows.

BackgroundShapefiles are a geospatial vector data format used to indicate geographic features in geographic information systems (GIS) software. Shapefiles are used in high-throughput phenotyping plant breeding and agronomic studies to identify plots from aerial imagery and extract remote sensing data. However, the process of manually creating shapefiles is tedious and error prone. Current options that assist in shapefile generation suffer from issues such as installation processes that require a degree of programming knowledge or inefficient methods for incorporating plot-level information from field books. In this study, we have developed a program called ‘SHP Buddy’, a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research.ResultsSHP Buddy is a free and open source QGIS plugin that is easily downloaded directly from the QGIS plugin repository. It provides options for generating serpentine replicated and unreplicated experimental layouts. Further, SHP Buddy is the first of its type to provide an intuitive method for removing non-experimental plots, such as non-experimental “fill” plots at the end of experiments or plots in irrigation wheel tracks. Plot information is easily incorporated by uploading a field book CSV file that contains a column of matching plot numbers. Lastly, plot dimensions can be modified to produce more precise regions of interest.ConclusionsSHP Buddy substantially reduces the time and increases the accuracy of shapefile generation. This results in reliable shapefiles that improve record keeping and the quality of high-throughput phenotyping data extracted. By working natively in QGIS, SHP Buddy provides an efficient solution to shapefile generation while maintaining a low learning curve.

ABSTRACT Powered by the emerging large language models (LLMs), autonomous geographic information system (GIS) agents can perform spatial analyses and cartographic tasks. However, a research gap exists in enabling these agents to autonomously discover and retrieve the necessary data for spatial analysis. This study proposes an autonomous GIS agent framework capable of retrieving required geospatial data by generating, executing, and debugging programs. The framework, with an LLM-driven decision core, selects data sources from a predefined list and fetches data using source-specific handbooks that document metadata and data retrieval details. Designed in a plug-and-play style, the framework allows human users or automated data crawlers to add new sources by creating additional handbooks. A prototype agent based on the framework is developed and released as a QGIS plugin and a Python program. Experiment results demonstrate its capability of retrieving data from various sources, including OpenStreetMap, administrative boundaries and demographic data from the U.S. Census Bureau, satellite basemaps from ESRI World Imagery, global digital elevation model (DEM) from OpenTopography.org, weather data from a commercial provider, and the COVID-19 case data from the NYTimes GitHub. This study is among the first attempts to develop an autonomous GIS agent for geospatial data retrieval.

TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models

The article presents the results of research comparing edge detection methods in digital images and verifying their usefulness in the context of the automatic vectorization process. As part of the experiment, well-known edge detection algorithms based on the analysis of derivatives of image quality functions (Sobel, Canny, Kirch) were implemented. The research problems of the article in the case of building detection basically boil down to the identification of homogeneous areas, the detection of edges or points in a digital image. The original program developed in the Matlab environment made it possible to obtain a description of the edges and their approximation with straight lines, as well as to analyze the quality of the obtained results. In addition, the validity of using neural networks was also analyzed in this context. The neural networks used an algorithm obtained from the GitHub hosting website and implemented as a plug-in for QGIS 3.26. Another attempt at algorithmic image analysis was based on the use of the GAN technique, i.e. the use of a generative network architecture that acts as an algorithm using the potential of two mutually opposed networks whose task is to generate a synthetic result. Under this assumption, one network is the so-called data generator and the other is the discriminator, critically assessing the generating network for authenticity. For each algorithm, the accuracy of vectorization of the detected edges was calculated. The most promising in this respect was an artificial intelligence algorithm using the technique of generative adversarial networks.

HighlightsNewly developed interactive QGIS tools automate and optimize depth placement of drainpipes, reducing the complexity of subsurface drainage design.These tools accurately estimate pipe sizes for each drainage segment, sequencing both downstream-to-upstream and upstream-to-downstream.User-friendly and exportable outputs enable efficient decision-making, cost analysis, and seamless integration with drainage installation machines.Abstract. Our suite of QGIS tools addresses the crucial final aspects before the installation of subsurface drainage systems, essential for effective water management in agriculture, civil engineering, and land development. Accurate depth placement and sizing of drainage pipes significantly impact system efficiency, enhancing overall land productivity. Developed to meet this demand, our tools specialize in estimating the installation depths and sizing subsurface drainage pipes. The primary objective is to generate buried elevation depths and pipe sizes for each line segment within the drainage network, sequencing downstream-to-upstream and upstream-to-downstream, respectively. Determining minimum drainage main sizes relies on factors like burying order, end-point elevations, distances, and specified elevation depths and slopes. These user-friendly QGIS tools, utilizing Tile Order, Cumulative Flow Lengths, Burying Slope, and specifications like drain spacing and material, are vital for accurate pipe sizing. Burying and sizing data is exportable in various formats for drain installation machines, and with built-in cost analysis features, they facilitate informed decision-making and budget planning for drainage projects. Optimized for compatibility with all QGIS3 versions, these tools are freely available for download from the QGIS Plugin Repository. Successfully tested with the University of Illinois' South Farm drainage system, they effectively simplify the burying and sizing processes for drainage networks. Keywords: Drainage coefficients, Elevation depths, Pipe estimations, Pipe sizing distributions, Spreadsheet exports, Tile burying systems.

Development of QGIS plugin for flood inundation mapping: applying Otsu’s thresholding technique