GeoPython 2023 Talk List

This is a preliminary list of talks/workshops. Some things may still change.


A guide on using OSMnx and geopandas to extract and manipulate OpenStreetMap data

Matt Travis

A look at the process of extracting and loading OpenStreetMap building footprints into PostGIS using OSMnx and Geopandas. This talk will cover our particular use case and the workflow we used.

Assessment of Nitrogen dioxide and Carbon monoxide concentrations over Oman during (2018-2019)


This project aims to measure NO2 and CO pollutants and detect the most polluted areas in Oman during 2018 and 2019.

Automated Isozones computation with OpenRouteService and Python

Hans-Jörg Stark
Swiss Army

The presentation talks about the possibilities how to use Python and OpenRouteService to compute Isozones for further GIS analysis. The contents of this presentation is very practical.

Automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images

Daniel Scheffler

The talk presents AROSICS and SpecHomo, two open-source and easy-to-use Python packages for automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images.

Automated weeding of railway tracks

Sven Schmitz-Leuffen
ARx iT

Automated management of the spraying / weeding of railway tracks and their surroundings by dedicated SNCF locomotives and wagons equipped with specialized geolocated equipment.

Automatic sill height detection from Mobile Mapping point clouds data

Milad Mahour
Municipality of Rotterdam

Mobile Mapping point cloud object detection is becoming vital and essential for many real-world applications when it comes to precisely measure and detect critical objects. The municipality of Rotterdam is responsible for road construction details and maintenances of streets in Rotterdam. For example, the municipality will try to take into account the slope of the street. In this way they ensure that the new street or newly installed elements do not cause any disruption to water drainage.

Beyond Boredom: A Layered Approach To Automating Borehole Analysis

Ben Jones, John Sandall
Mott MacDonald

You've created a ground-breaking Python script which only you can run. Your colleagues don't know Python and cloud deployment isn't an option, but with a little digging we can still unearth approaches for script automation within a low-tech IT environment.

CLIMADA: a python package for physical climate risk analysis

Alessio Ciullo, Chahan Kropf

This talk will introduce CLIMADA [1], an open-source and -access modeling platform for climate risk assessments. Using state-of-the-art probabilistic modelling, CLIMADA allows to estimate multi-hazard socio-economic impacts as a measure of risk today, the incremental increase from socio-economic development and the further incremental increase due to climate change.

Code-Centric Infrastructure as Code (IaC) using Pulumi with Python

Anmol Krishan Sachdeva

Interested in handling your Cloud Infrastructure through code like you handle the application logic and software? Enter Pulumi, a universal Infrastructure as Code platform that helps you maintain your multi-cloud Infrastructure as Code using popular languages like Python, Java, Golang, .NET, Typescript, etc.

This session focuses on writing Pythonic Infrastructure as Code with Pulumi.

Deep Internal Learning for Satellite Image Synthesis

Mikolaj Czerkawski

An overview of deep internal learning methods applied for satellite imagery with examples of open-source PyTorch implementations.

Developing an AI assisted collaborative mapping tool for disaster management

Arno Röder
Machine Learning Engineer | GIS | Forest Science

This talk will present the results of the OMDENA challenge "Developing an AI assisted collaborative mapping tool for disaster management".

Efficient Sentinel-2 Data Acquisition with S2Portal: A Python-Based Tool for Customized Area of Interest Downloads

Julia Neelmeijer
GFZ German Research Centre for Geosciences

We present S2Portal, an open-source tool that enables efficient downloading of Sentinel-2 data specifically for custom areas of interest (AOIs) from the AWS Public Dataset Program. S2Portal respects user-specified criteria such as date range, spectral bands, and cloud cover, and only downloads the minimal amount of data needed to cover the AOI, reducing the time and cost of data acquisition.

Engaging geovisualisations with Altair

Mattijn van Hoek

Altair is a powerful toolkit for creating interactive and engaging geovisualisations in Python. Lets talk about it.

FLORIA, a custom python pipeline for urban flood extraction from SAR multi-sensors, supported by U-Net convolutional network.

Ari Jeannin

This talk will present FLORIA, an innovative custom python pipeline for urban flood extraction. The implementation of the predefined workflow, ingesting most SAR sensors and extracting from U-Net convolutional network approach, will be highlighted. Thanks to open-source data, software and libraries, mapping and automatizing urban flood extraction from SAR imagery is now possible.

Fast API and Tile38: Ultra fast retrieval of geospatial data

Ganesh N. Sivalingam

In this talk we will explore Tile38, Fast API briefly, and how to combine them to create quick APIs for delivering geospatial data.

Generating Clouds in Satellite Images with SatelliteCloudGenerator ☁️

Mikolaj Czerkawski

A run through a PyTorch-based tool for generating clouds in satellite images.

Generating Elevation Data from Stereo Pairs of Satellite Imagery

Yoni Nachmany

An overview of Python libraries that produce Digital Surface Models (DSMs) from stereo satellite imagery

Geospatial Analytics for climate change risks and opportunities

Abdul Khaleq Abdullah Al-Qasaily
GeoSpace AI

Using geospatial analytics tools of geopython and ML , Earth Observations, we identify climate change risks on energy sources and effect on communities and urban smart cities development

HoloViz: Visualization and Interactive Dashboards in Python

Sophia Yang

Do you use data visualization to understand data and tell stories? Do you need to visualize big geospatial datasets? Are you interested in leveling up your visualization skills with HoloViz? HoloViz is a high-level Python visualization ecosystem that gives you the superpower to help you understand your data at every stage needed.

How to use JavaScript Google Earth Engine in Python

Mathieu Gravey

The Open Earth Engine Library (OEEL) is a collection of code goodies for Google Earth Engine (GEE) build on top of the JavaScript API. But recently a feature was added to "map" all this function and most other JavaScript functions in Python.

Illuminating the Underground with Artificial Intelligence.

Adonai Vera
Subterra AI

Map, Analyze, and Forecast your underground infrastructure using Subterra imaging solutions and our Terralytics™ Cloud Platform.

Introduction to depth-based stable diffusion for Blender using Dream Textures

Lukas Schmid

The field of GAN based generation of text has been extended to images with stable diffusion and even 3D with NVIDIA's GET3D. I'll present the newest findings and some possible applications in BIM and GEO-Visualisation

MapScale: a Python-based technology for automated conversion of drawings to indoor maps

Melih Peker, Ege Cetintas

In this talk, we will demonstrate our patented, learning-based technology (MapScale) for automated conversion of technical drawings to indoor maps.

Measuring long-term urbanization (1900 – 2020) using (open) geospatial data sources: Python-based strategies for data integration, analysis, and visualization

Johannes Uhl
University of Colorado Boulder

How to measure the long-term evolution of urban landscapes? We present our ongoing work on geohistorical urban data to visualize and analyze the dynamics of urban settlements using Python-based tools.

Open Source in Environmental Sustainability

Tobias Augspurger

This study provides the first analysis of the open source software ecosystem in the field of sustainability and climate technology. Thousands of actively developed open source projects and organizations were collected and systematically analyzed using qualitative and quantitative methods as part of the Open Sustainable Technology project.

Power of Python in Geodata Integration

Lassi Lehto
Finnish Geospatial Research Institute

A Python-based data integration platform enables cross-border access to 3D buildings datasets, critical for renewable energy-related use cases.

Predicting Spatial growth of urban area using Fourier analysis and neural network

Muhammad Athar Javaid
University of the Pujanb, Pakistan

Urban growth prediction in urban planning is a core parameter. In this talk, we present a way to predict the new urban boundary based on the prediction of the length of vectors and their rotational frequency using a neural network. The core idea is to take the boundary of the urban area as a Fourier series and therefore represent it as a sum of several rotating vector terms. The length of the vectors and the rotating frequency act as the training parameters for a neural network. The training parameters for the boundaries of years 2005, 2010, 2017, and 2022 are used to train the network. Then the trained network is used to predict the boundary of the year 2025.

R Spatial and GeoPython, a happy marriage

Martin Fleischmann, Edzer Pebesma
Urban and Regional Laboratory, Charles University

We discuss differences and correspondences between the R and Python spatial software ecosystems, upstream libraries, and efforts and opportunities to further mutually benefit each other.

Real-time Stream Processing without Migraines

Fawaz Ghali

As businesses grow, many applications will at some point run into scaling issues — what works fine at low transaction, volumes don’t work well, or at all, at high volumes. Real-time technology solutions like the open-source Hazelcast Platform have helped many businesses overcome their scaling issues to provide high throughput and low latency at tremendous scale

Revolutionizing Demography with AI’s Deep Learning: Using Python to Estimate Population Dynamics

Lisah Khadiala Ligono

Python and Artificial Intelligence techniques, including deep learning and convolutional neural networks, can be used to improve demographic modeling in times of crisis or rapid change. Using the crisis in Venezuela as a case study, I demonstrate the utility of this approach in informing response efforts and decision-making.

Shapely 2.0

Joris Van den Bossche

Shapely 2.0 has finally arrived! Several years in the works, Shapely 2.0 is a major release, featuring a large refactor and fast, vectorized (element-wise) array operations along with many other improvements.

Spherely: Geometric features on the sphere

Benoît Bovy

Spherely provides Python/Numpy vectorized bindings to the S2Geometry library for the manipulation and analysis of spherical geometric objects. Complementary to Shapely 2.0, it may be used as a backend geometry engine for Python geospatial libraries like GeoPandas.

Strategies and Software for Robust Color Palettes in (Geo-)Data Visualizations

Reto Stauffer
Universität Innsbruck

Color plays a crucial role in visualizations in the sciences and a well-designed color map is important for inclusivity, effectiveness, and clear communication of information. While many software packages nowadays provide adequate pre-defined color maps and defaults, we will discuss strategies and introduce software for designing and evaluating customized color maps that are suitable for a wide range of tasks and applications.

Timebased visualization of costs, surface-level and volume geometry

Lukas Schmid

A fully OpenSource Project-controlling Dashboard unifying GIS, IFC, construction schedule and object-level information

UNIGIS Girona, an MSc for geospatial data scientists

Josep Sitjar
SIGTE - University of Girona

New trends in the geospatial sector have motivated a restructuring of the UNIGIS Girona MSc GIS programme, and since 2020 we’ve been working hard on the design of new contents and subjects. In this talk we’ll show how new trends in the geospatial sector have motivated the redesign of our programme as well as Python’s role in this new approach.

Urbantrips: an opensource library to analyze public transit smart card data

Felipe Gonzalez
Interamerican Development Bank

Urbantrips is an open-source library that takes information from a public transportation smart card payment system and produces origin-destination matrices and some KPIs for bus routes. Work behind this involves inferring destinations, creating chain trips and several spatial transformations using H3, and Pandas optimization and parallelization processes to make it more performant.

Using Python based GeoAI to Enhance topographic mapping in the Dutch National Mapping Agency

Stefan Bussemaker, Ditmar Visser

In this talk, we present our journey in creating automated pipelines for data preparation and training GeoAI models.

Using Temporal geo-data inside QGIS with Python


The aim of this talk is to showcase how one could using Python and QGIS to access and visualize Temporal geo data using the QGIS Temporal Controller Python API. The session will also provide guide on PyQGIS Temporal API, python scripting inside QGIS, how to build standalone python applications that uses and how to create QGIS python plugins that can help in access and visualization of the temporal geo-data.

Vector data cubes as a bridge between raster and vector worlds

Martin Fleischmann
Urban and Regional Laboratory, Charles University

This talk introduces the concept of vector data cubes - multi-dimensional arrays where at least one dimension is composed of vector geometries - and its implementation in Python within a new library Xvec, built on top of Xarray, Shapely 2.0 and GeoPandas.


Building your own Machine Learning Platform from OSS

Natu Lauchande
Workshop 90 Minutes

This workshop will explore the engineering aspects of machine learning, including best practices for designing and building machine learning systems. We will discuss the architecture of well-known machine learning platforms like Michelangelo at Uber, Bighead at Airbnb, and FB Learner at Facebook, and use MLFlow, Feast, Ray, Iceberg and Python to create an open-source version of these systems that can be used by tech startups. The complete process of training, deploying, monitoring, and testing machine learning models will be covered, as well as strategies for avoiding technical debt.

Geospatial Data Processing in Python

Martin Christen
Workshop 90 Minutes

In this workshop, you will learn about the various Python modules for processing geospatial data, including GDAL, Rasterio, Pyproj, Shapely, Folium, Fiona, OSMnx, Libpysal, Geopandas, Pydeck, Whitebox, ESDA, and Leaflet. You will gain hands-on experience working with real-world geospatial data and learn how to perform tasks such as reading and writing spatial data, reprojecting data, performing spatial analyses, and creating interactive maps. This tutorial is suitable for beginners as well as intermediate Python users who want to expand their knowledge in the field of geospatial data processing

Semi supervised classification for aerial imagery

Itzá Alejandra Hernández Sequeira
Workshop 90 Minutes

With the help of current semi-supervised learning algorithms, we can classify aerial scenes using 4, 20, or 40 labeled examples per class and still obtain similar accuracies as training with numerous labeled examples. With this workshop, we want to show the semi-supervised learning algorithms currently available and how to use the repositories available for scene classification.

Visualize, analyze and compare geographical datasets with EOmaps.

Raphael Quast
Workshop 120 minutes

This workshop will introduce the basics of EOmaps, a package that aims to simplify geographical data-analysis. You will learn how to create basic maps (plotting data, add features etc.) , and how you can directly use the maps as interactive data-analysis widgets.

Writing an efficient code for GeoPandas and Shapely in 2023

Martin Fleischmann
Urban and Regional Laboratory, Charles University
Workshop 120 minutes

With the release of Shapely 2.0, the GeoPandas-based code that have been optimised years ago may no longer provide the best performance. This workshop will show you how to change that and write efficient and convenient GeoPandas code that uses the benefits of the latest developments in the Python geospatial ecosystem.