TreeEyed was developed to facilitate tree monitoring in silvopastoral systems as an open source and easy to use QGIS Plugin that leverages state of the art AI models for tree detection, tree segmentation and canopy height estimation. Over six thousand downloads from >20 different countries from the QGIS plugin repository demonstrate great interest and user feedback has been useful to identify different areas of improvement. This software update to version 0.2.0 improves the processing and usability of the plugin in terms of model integration capabilities, tiling approach and cache system, user interface improvements with the addition of QGIS processing algorithm, and features for developers.

MyLiDAR: Improving LiDAR data processing and analysis with a comprehensive open-source QGIS plugin

Satellite images enable the analysis of the spatial and temporal distribution of burned areas (BA) and burn severity (BS) to quantify the impacts of wildfires. However, the generation of reliable maps requires algorithms and tools available to the user for regional to global analyses. We present a public QGIS plugin (BAD, Burned Area Detector) for automatic BA and BS mapping using pre- and post-fire Sentinel-2 multispectral images. The plugin also incorporates a validation module for assessing the accuracy of output maps. The BA detection is based on a multi-criteria soft computing approach that incorporates experts’ fuzzy knowledge and its integration, combined with a region growing algorithm. BS is estimated using the difference of the Normalized Burn Ratio (NBR) index.The plugin was tested on wildfire events in Spain (summer 2022) and California (winter 2025). Besides proving the functionalities of BAD, these test cases confirm the robustness of the algorithm when applied automatically to Mediterranean regions and its flexibility in ingesting different data sources (active fires as seeds for the region growing). Results across all studied areas show an average omission error of 9.57%, a commission error of 16.56%, and a Dice coefficient of 89.62%.

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

TRIGRSMap: a QGIS plugin for spatio-temporal rainfall-induced landslide susceptibility mapping

Abstract Geospatial analysis often treats data distributed over spherical surfaces, necessitating specialized statistical methods. Traditional planar Euclidean statistical tools, however, fail to accurately capture directional patterns inherent in such data, resulting in distortions or an oversimplification of directional patterns when applied to curved contexts. This paper introduces QSphericalStats, a QGIS plugin designed to perform robust spherical statistical analyses through the use of an accessible, user-friendly interface. By extending QGIS’s native geospatial capabilities, QSphericalStats enables the analysis of three-dimensional directional data, such as orientations derived from digital elevation models (DEMs). Two case studies were conducted to assess the plugin’s effectiveness in practical scenarios. The first compares LiDAR DEMs and ASTER GDEM datasets, demonstrating the tool’s accuracy in analyzing curved surface data; the second examines cartographic maps with associated 3D information, highlighting its ability to extract and interpret directional trends from complex geospatial datasets. Results from both studies show stable mean direction estimates and moderate concentration parameters, underscoring the plugin’s robustness and the added interpretative value of spherical statistics in geospatial research.

Reconstructing the palaeogeography and palaeotopography of the Earth has been a challenge since the advent of the plate tectonics theory in the 1960s.With the development of geographic information systems (GIS), many plate tectonics models have been created and allowed researchers to reconstruct the movements of plates back in time (up to 1 billion years for some models), based on geological evidence found in the present-day Earth.We present TopoChronia, a QGIS plugin that converts input data from plate tectonic models into quantified and synthetic topography.This plugin is optimized to work with the PANALESIS model, because it is the only one currently providing sufficient information in terms of geological features to reconstruct a fully quantified topography.

Effective water management requires frequent monitoring, but traditional methods are limited in spatiotemporal scope. While satellite remote sensing provides extensive data, a gap persists in accessible, integrated software for non-specialists. To address this, we developed the RS-WaterQuality Mapper, an open-source Python plugin for QGIS. This toolbox provides a complete, scientifically robust workflow for aquatic remote sensing, from aqua-focused atmospheric correction to the application of advanced machine learning models. A key innovation is the implementation of a multi-predictor ensemble model based on spectral-space partitioning, which enhances predictive accuracy in optically complex inland waters. Built on optimized Python libraries and a multi-processing architecture, the tool ensures computational efficiency for processing large satellite scenes. The toolbox's utility was validated in diverse case studies-a U.S. reservoir, a Kenyan saline lake, and a U.S. river system-demonstrating strong performance (R 2>0.80). By embedding state-of-the-art science into a familiar GIS environment, the RS-WaterQuality Mapper empowers a global community of researchers and water resource managers to leverage satellite data for more effective ecosystem management.

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.

Abstract. Extreme weather events such as heavy rains are an increasing challenge. The potential impact of flooding on residential buildings can be simulated using digital twins. However, when using geometric-semantic information from diverse data resources, such as 3D city models, zoning or cadastre, the data must be carefully selected and programmatically prepared for the simulation. In this study, we present how a use-case driven classification was generated for the residential buildings in the city of Dresden, which is used to estimate the damage potential. The research focuses on both the supervised building classification with a neural network and the open source software framework. Data management is done with the 3DCityDB in PostgreSQL. QGIS is used for visualisation and user interaction. The Python-plugin automatically classifies more than 70,000 residential buildings based on 37 residential building classes. The hierarchical classification is challenging due to the ground truth sample size of about 21,000 and the heterogeneous distribution of the samples. The core of the method is the training and validation utilising random forest as machine learning method. With the developed toolset, classification results can be visually checked in a subsequent step using QGIS. Additionally, the classification, might be corrected manually for individual buildings using mobile mapping data, if necessary. Eventually, the assigned classes are fed back into the official CityGML city model as a new attribute, enabling a realistic damage potential analysis, in a free and publicly available 3D-WebGIS platform. The project is funded under the Smart Cities pilot programme of Germany.

To improve the scientific basis and accuracy of regional landslide risk identification, the uncertainty of geotechnical parameters in shallow landslides triggered by earthquakes is considered. An improved First Order Reliability Method (FORM) is used to develop a probabilistic framework for regional landslide susceptibility assessment. A physical model of an infinite slope under seismic loading is constructed using a pseudo-static approach, where key soil parameters are treated as random variables to quantify the impact of uncertainty on landslide failure probability. A QGIS plugin, QGIS-FORM, is developed in Python to automate the generation of regional susceptibility maps based on failure probability (<inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mtext>f</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula>) and factor of safety (<italic>FOS</italic>). Using the Maerkang earthquake-induced landslides as a case study, key parameters—including slope, peak ground acceleration, and geological lithology—are analyzed. A comparison is made between FORM and the Mean First Order Second Moment method (MFOSM). The predictive performance of both <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mtext>f</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> and <italic>FOS</italic> is evaluated under different buffer zone sizes and levels of coefficient of variation (<italic>COV</italic>), using receiver operating characteristic (ROC) curves and balanced accuracy (BA) as evaluation metrics. Results indicate that FORM performs better than MFOSM in addressing the nonlinear behavior of complex slopes, showing an improvement of 5.5% in AUC. Under different buffer sizes, the AUC values for <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mtext>f</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> are 82.9%, 84.1%, and 85.0%, all exceeding those of <italic>FOS</italic>. BA analysis shows that with increasing <italic>COV</italic>, the optimal <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>P</mml:mi> <mml:mtext>f</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> thresholds for FORM are 0.08, 0.2, and 0.27, each corresponding to a maximum BA of 0.704. These findings suggest that while <italic>COV</italic> influences the sensitivity of threshold selection, it does not compromise the model's predictive performance. The FORM-based method accounts for input uncertainty and provides more stable and detailed landslide risk zoning. It offers a scientifically grounded visualization tool for landslide risk management.

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 It is widely documented that rivers and their ecosystems are subject to enormous pressure worldwide. They are affected by various human activities, and climate change. These drivers are characterized by complex (inter-)dependencies and processes, which need to be addressed for successful river management. This requires data, tools and capable stakeholders to use them. Therefore, the aim of this paper is 1) to provide a comprehensive overview of challenges in river management, 2) to elaborate the central role of geographical information systems (GIS) in addressing these challenges and 3) demonstrate the need for GIS related capacity building for stakeholders. Part 1 and 2 of the paper provide an interdisciplinary literature review, citing case studies and theoretical discussions on spatial aspects and tools for river management. Part 3 illustrates potentials of GIS training for institutional capacity building based on interviews, surveys and practical experience from a current project, in which the authors were involved. The evaluation of the project indicates that the participants could archive considerable improvements in their ability to apply GIS methods in river management. The project also developed a QGIS plugin and guidelines for river geodata to facilitate collaboration between local stakeholders.