This is a preliminary list of talks/workshops. Some things may still change.
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.
This project aims to measure NO2 and CO pollutants and detect the most polluted areas in Oman during 2018 and 2019.
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.
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 management of the spraying / weeding of railway tracks and their surroundings by dedicated SNCF locomotives and wagons equipped with specialized geolocated equipment.
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.
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.
This talk will introduce CLIMADA , 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.
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.
An overview of deep internal learning methods applied for satellite imagery with examples of open-source PyTorch implementations.
This talk will present the results of the OMDENA challenge "Developing an AI assisted collaborative mapping tool for disaster management".
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.
Altair is a powerful toolkit for creating interactive and engaging geovisualisations in Python. Lets talk about it.
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.
In this talk we will explore Tile38, Fast API briefly, and how to combine them to create quick APIs for delivering geospatial data.
A run through a PyTorch-based tool for generating clouds in satellite images.
An overview of Python libraries that produce Digital Surface Models (DSMs) from stereo satellite imagery
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
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.
Map, Analyze, and Forecast your underground infrastructure using Subterra imaging solutions and our Terralytics™ Cloud Platform.
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
In this talk, we will demonstrate our patented, learning-based technology (MapScale) for automated conversion of technical drawings to indoor maps.
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.
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.
A Python-based data integration platform enables cross-border access to 3D buildings datasets, critical for renewable energy-related use cases.
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.
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.
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
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 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 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.
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.
A fully OpenSource Project-controlling Dashboard unifying GIS, IFC, construction schedule and object-level information
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 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.
In this talk, we present our journey in creating automated pipelines for data preparation and training GeoAI models.
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.
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.
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.
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
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.
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.
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.