NSCES RESEARCH & SPATIAL DATA

Real-world data for
spatial discovery.

Explore the relationship between dissertations, geospatial datasets, and reconstructed geometry. A unified repository for academic research and spatial context. Access high-quality spatial datasets and in-depth dissertations in one place.

12 Published Datasets
217 Dissertations
4 Departments
217 Researchers

About the NSCES Repository

The NSCES Repository is the official digital repository of the Ndata School of Climate and Earth Sciences (NSCES) at the Malawi University of Science and Technology (MUST). It serves as a centralized platform dedicated to preserving and providing open access to academic research and geospatial data. By bridging the gap between in-depth academic dissertations and real-world spatial context, the repository unifies high-quality geospatial datasets, reconstructed geometries, and scholarly publications in one place. This platform empowers researchers, policymakers, and students to explore spatial data, drive scientific discovery, and make informed, data-driven decisions for sustainable development and climate resilience.

WHAT THE DATA ACTUALLY REPRESENTS

Discover imagery.
Derived spatial structure.

Use both, with the right expectations.

Connected does not mean independently measured.

Spatial datasets encompass a variety of formats, including vector data (points, lines, polygons), raster imagery, and derived 3D structures like photogrammetric point clouds and meshes. While these structures are reconstructed from imagery, they are not independent survey ground truth. Camera files can contain observations, calibration or reconstructed poses; inspect the actual fields and metadata to understand the data's limitations.

Local context for Malawian spatial datasets.

The current catalog primarily features aerially captured scene data (drone and UAV imagery) focused on Malawian landscapes, including agricultural estates, urban developments, and land administration areas. These datasets are derived from local flights and are not a promise of indoor coverage, global satellite precision, or simulator-ready assets. Users should account for local atmospheric conditions and typical equatorial lighting when interpreting these derived structures.

How to evaluate a spatial dataset