DEEPTAL is a geospatial analysis tool developed to identify subglacial overdeepenings where future glacial lakes are likely to form as glaciers retreat and ice melts. The name combines “deep”, representing overdeepened terrain, with “taal” (the Hindi term for lake), highlighting its focus on deep hollows in glacier beds capable of storing water.

DEEPTAL is a free software tool developed in R with an interactive Shiny-based graphical user interface that integrates SAGA GIS for terrain preprocessing and hydrological analysis.

Designed for glaciology researchers, hydrologists, and geospatial analysts, DEEPTAL requires inputs such as Digital Elevation Models (DEMs), glacier ice thickness data, and glacier outlines to identify potential glacial lake formation zones through automated geospatial workflows.

Sample inputs are given for practice. To download the tool and for other details click here.

The RESERVOIR MAPPER (Remote sEnsing based multi-sensor Spatio-tEmporal fusion aided ReserVOIR MAPPing framEwoRk), built entirely on the Google Earth Engine (GEE) platform, is designed to generate temporally continuous reservoir water spread time-series across diverse climatic and geographic regions worldwide. By directly addressing the persistent challenges posed by cloud cover over optical sensor-derived water scenes, the framework integrates freely available, high-resolution optical (Sentinel-2) and Synthetic Aperture Radar (Sentinel-1) satellite datasets. To overcome cloud limitations, it utilizes a multi-sensor spatio-temporal fusion strategy where cloud-contaminated optical pixels are automatically replaced by water surface data extracted from the temporally nearest SAR scene.

AutoICE is an automated, user-friendly standalone software tool designed for Windows to estimate glacier ice depth, bed topography, and ice volume in mountainous regions. It is an automated implementation of the VoICE (Velocity-based ICe thickness Estimation) model, which belongs to velocity-based Shallow Ice Approximation (SIA) approaches.

Accessibility and Scalability: AutoICE provides a simple graphical user interface that simplifies complex ice thickness estimation workflows. It is designed for large-scale applications and can process multiple glaciers efficiently using commonly available geospatial datasets such as Digital Elevation Models (DEM), glacier masks, and surface slope.

Customization: The tool allows users to tune key model parameters based on the glacier region of interest. In particular, shape factor parameterization can be adjusted, making it suitable for data-scarce glacier environments.

Built-in Uncertainty Assessment: AutoICE integrates Monte Carlo simulation to quantify uncertainty in ice thickness, bed topography, and volume estimates, providing statistically robust outputs.

Detailed Results: The tool generates spatial outputs such as ice thickness and bed topography maps, along with non-spatial outputs including organized Excel sheets of glacier-scale statistics and volume estimates.

GitHub: AutoICE Repository

GeoGuru is an engaging learning app designed to introduce school students to the fundamentals of Geospatial Technology. It transforms traditional map reading into an interactive experience where students actively create, analyze, and understand maps. By using GeoGuru, students gain practical skills in mapping, spatial thinking, and measurement, making geospatial learning hands-on and relevant to real-world applications.

We invite you to download the GeoGuru App, try it out, and provide your valuable feedback for further improvements.

Glacier Bed Topography (GlabTop2_IITB) is an independently implemented GlabTop2 ice thickness model based on the concept of Shallow Ice Approximation (SIA) and the assumption of perfect plasticity. To estimate glacier ice thickness, this model approach requires only the Digital Elevation Model (DEM), surface Slope, and glacier Mask. The GlabTop2_IITB version includes a graphical user interface (GUI) written in the MATLAB programming language. The GUI was designed with the goal of making it usable by people who are not programmers. In addition, a simple method is proposed for estimating an optimal/near-optimal model parameterization of shape factor, which is one of the model inputs. The proposed glacier parametrization method was validated using field measurements, which demonstrated its robustness on glaciers lacking field ice thickness measurements. Overall, the GlabTop2_IITB version is semi-automated, features novel shape factor parametrization, and is applicable to any valley glacier.

It is available as ICEBED Tool (ICe volumE and Bedtopography Estimation from DEM) in GitHub

The Neuro-Fuzzy Flood Mapping from SAR (NeFFSAR) package provides the codes for the production and validation of flood maps from Level-2 single SAR imagery. This independence from supporting ancillary datasets makes the algorithm easily applicable to operational scenarios. The work flow first optimizes window sizes and then extracts omnidirectional grey level co-occurrence matrices (GLCM). These GLCMs are subsequently used to derive texture features which are optimized using an independent component transform for dimensionality reduction. A supervised neuro-fuzzy classifier is then used to assign fuzzy membership values of flooding to the SAR image enhanced with the optimized texture layers. Validation results suggest significant improvements especially in areas of rough soil or sparse tree cover.

The Indian WAtershed Management support System (IWAMS) is a QGIS plugin for making appropriate scientific decisions in the planning watershed management projects. IWAMS is designed to perform necessary analysis for planning watershed management projects through the following three modules. Hydrological analysis module to perform process-based soil erosion modelling. Prioritization module to identify critical area based on hydrology as well as poverty aspects. Conservation module for optimal selection and allocation of soil and water conservation measures.