PUBLICATIONS

Below is the list of all publications over the years published by the team of DEPRIMAP that are directly or indirectly linked to the project.

Journal Articles

Coming soon…

Conference Papers

Understanding Informal Settlement Transformation through Google’s 2.5 Dataset and Street View based Validation

Sai Ganesh Veeravalli, Jan Haas, John Friesen, Stefanos Georganos

In 8th Workshop on Global South at 44th EARSeL Symposium

doi: https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-245-2025

Full citation: Veeravalli, S. G., Haas, J., Friesen, J., and Georganos, S.: Understanding Informal Settlement Transformation through Google’s 2.5D Dataset and Street View based Validation, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 245–251, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-245-2025, 2025.

Code repository: https://github.com/saiga143/urban-change-google-25d

Zenodo repository: Veeravalli, S. G. (2025). Codebase for urban change detection in informal settlements using Google’s 2.5D dataset (v1.1). Zenodo. https://doi.org/10.5281/zenodo.15269825
—
Towards a Spatial Measure of SDG 11.1.1: Open Data for Urban Deprivation Mapping

Sai Ganesh Veeravalli, Florencio Campomanes V, Sebastian Hafner, Stefanos Georganos, Monika Kuffer, John Friesen, Dana R Thomson, Robert Ndugwa, Dennis Mwaniki, Angela Abascal, Peter Elias, Tobi Eniolu Morakinyo, Julio Pedrassoli, Gabriel de Oliveira, Anthony Boanada-Fuchs, Boris Zerjav, Juan Manuel D’Attoli

At Joint Urban Remote Sensing (JURSE) 2025, Tunis Conference

doi: http://dx.doi.org/10.1109/JURSE60372.2025.11076033

Full citation: S. G. Veeravalli et al., “Towards a Spatial Measure of SDG 11.1.1: Open Data for Urban Deprivation Mapping,” 2025 Joint Urban Remote Sensing Event (JURSE), Tunis, Tunisia, 2025, pp. 1-4, doi: 10.1109/JURSE60372.2025.11076033.

Doctoral Research Experiments

Efficient Access to Google Buildings V3: A Two-Step Workflow for Targeted Downloads and Processing

Google Buildings V3 is a powerful dataset, but at 178 GB, it's not easy to work with. In this blogpost, we introduce a two-step Python workflow to efficiently download and extract only the building footprint data you need — using your own region of interest. No need to process the world — just get the tiles that matter, filter them, and save clean GeoPackages. Ideal for urban research, slum mapping, or large-scale city analysis. Lightweight, scalable, and open-source.

by Sai Ganesh Veeravalli

on Jul 23

Doctoral Research Experiments

Get Notified When Your Jupyter Notebook Finishes Using a Telegram Bot

Tired of waiting for Jupyter cells to finish? In this post, we show how to use a simple Telegram bot to get instant notifications when your notebook completes — no email setup, no complex libraries. A quick, beginner-friendly solution for long-running geospatial or ML tasks.

by Sai Ganesh Veeravalli

on Jul 16

Doctoral Research Experiments

Going Beyond Earth Engine: Scalable Downloads of Google’s 2.5D Building Dataset

A lightweight, offline-friendly tool for downloading Google’s 2.5D Open Buildings dataset at national scale. This blog introduces a Jupyter Notebook that bypasses Earth Engine limits by using direct download links—enabling researchers to work with high-resolution building data across entire countries. Developed under the DEPRIMAP project.

by Sai Ganesh Veeravalli

on Jul 15

DEPRIMAP is a research funded by FORMAS (Swedish Research Council, application 2023-01210) involving KAU (Karlstad University, Sweden)