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.

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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.

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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.