GeoPython & PyML 2020

Registration will be in channel #registration-desk

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Remote Sensing has proved to be an important tool in monitoring our earth eco-system. The satellite imaging is a vital part of Remote Sensing. Predominantly, Satellite Imaging of the earth has been done in the optical domain and optical Images serve the majority of purpose for earth monitoring. But, these satellites do not have all-weather acquisition capability and this lacuna is filled by the satellite sensors working in the Microwave domain of the Electromagnetic spectrum. Synthetic Aperture Radar(SAR) is an Imaging Radar that acquires images of a particular area in the microwave region. This workshop deals with the processing of SAR Images and how these images can be beneficial in a variety of geographical applications.

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We present the findings of initial investigations into the use of open-source solutions for 3D ground-modelling within the context of the civil engineering industry.

Python libraries such as Sentinelsat, HARP and VISAN are very useful in order to download, analyze and visualize long series of remote sensing images. The objective of this talk is to expose how to use these tools to create a cartography map that reflects the mobility and industrial activity decrease during the COVID-19 lockdown.

The presentation is about findings and best practices in teaching geodata-processing with Python and OpenSource Libraries and proprietary packages

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With satellite imagery data so abundantly available in the public domain i.e the likes of Landsat-8, Sentinel-2, it's high time that we build automated systems to capture, analyze, and derive meaningful insights out of them. This talk would be about setting up an end-to-end pipeline with Python to search, process, and output insights from raw earth observation data.

This talk presents an Open Source, cloud-based, real-time geospatial data management system named XYZ that has recently received a native Python interface developed by the author which aims to make it more attractive to data scientists and analysts.

Geospatial Analysis as a way of answering questions about our world. Learning Geospatial Data, explains the major categories of data and several newer formats that are becoming more and more common. The Geospatial Technology Landscape, tells you about the geospatial technology ecosystem, which consists of thousands of software libraries and packages. Geospatial Python Toolbox, introduces software and libraries that form the basis of the book and are used throughout. Python and Geographic Information Systems, teaches you about simple yet practical Python GIS geospatial products. Python and Remote Sensing, shows you how to work with remote sensing geospatial data. Python and Elevation Data, demonstrates the most common uses of elevation data and how to work with its unique properties. The true power of geospatial analysis is modeling. Real-Time Data, examines the modern phenomenon of geospatial analysis.

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pygeoapi is an OGC Reference Implementation compliant with the OGC API - Features specification. pygeoapi supports many other OGC APIs via the Flask web framework and a fully integrated OpenAPI (REST) structure.

The goal of the Multi-Mission Algorithm and Analysis Platform of the European Space Agency (ESA-MAAP) is to bring together mission data with hosted processing and collaborative tools. In this talk I will show how we use the Open Source Scientific Python stack to design the platform and create the opportunity to build a community of users for new Earth Observation missions like ESA's BIOMASS mission.

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This talk aims to provide an historical context for the renewed interest in Discrete Global Grids (DGGs) by exploring what they do well and what they don't. In particular, we'll take a Python-centric look at what happens when we need to assimilate gridded data with data reported at the level of traditional administrative boundaries.

Demonstrate how use the Software FME ETL for vision computer

This analysis will try to get further insight on Bolivia's 2019 polemic results by applying standard ESDA methods with Python's Pysal library. To our knowledge, it would be the first work on this topic focusing on the geospatial dimension of the data

Registration will be in channel #registration-desk

The eternal question which haunts every aspiring data scientist is - Where should I begin? Is traditional machine learning still relevant in this era to solve business problems? In this tutorial we will address these questions and take a deep dive into applying some of the most widely used traditional machine learning algorithms on real life use cases using scikit-learn, mlxtend and pandas.

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Performing built-up classification using Google Earth Engine python API (for data collection) and XGBoost for classifiers

ML? API? AutoML? Python is the language of choice to solve problems with machine learning, but what can you build in only a few hours? In only a few days? Without any expertise?

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Dual Attention Residual Band Selection Network for Spectral-Spatial Hyperspectral Image Classification

To analyze the use of satellite SAR data and its comparison to optical imagery for identification and classification of burnt and unburnt patches after a forest fire.

In this work, we investigate the ability of modern computer to provide an automatic extraction of fish age from otolith images. The dataset used in this work is provided from the database of the Hellenic Center of Marine Research (HCMR) and includes a large collection of 5027 otolith images and measurements of length for red mullus fish species.

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The big data landscape is vast and continuously growing and it is also becoming aware of the challenges when dealing with geospatial data. Here I will go over and compare the recent open source developments in this space that enable working with geospatial data at scale.

A novel method for detecting solar panels and its geometry on aerial imagery is presented. Deep Learning with PyTorch is being used for segmentation. The goal is to know the exact locations, dimensions and potential of every solar installation in Switzerland.

Complete Python package to monitor the water quality of reservoirs and lakes. This complete solution enables data management along its life cycle, from the ingestion from different sources to the analysis and publication.

Today's sea ice forecasts are at a coarse spatial resolution of several kilometers, whilst tactical navigation planning requires forecasts of much higher resolution. We present an approach to couple satellite and model information, to produce high resolution forecast images.

Opening Session

Google Earth Engine is a powerful tool for earth science, but its main entrypoint is through a web-based JavaScript editor. Here we’ll talk about when and how to use Google Earth Engine in Python. The focus will be about getting data and connecting with popular frameworks such as Tensorflow for analysis. This talk is aimed towards geospatial data scientists trained in Python who are interested in using Google Earth Engine for their workflow and want to be immediately productive in using the tool.

To show how TurfPy can be used to perform various geospatial functionality on geojson data.

A python tool for creating ready to use stream cross-section data from high resolution elevation data e.g. for hydrologic modelling using geopandas, shapely and rasterio

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Identification and description of morphological patterns forming cities are becoming easier due to expansion of urban open data and advancements of the related software. This talk presents current developments of methods of morphological analysis based on open data, leading to a classification of distinct types of urban form patterns.

Shapely, the python package providing an easy Python interface for working with geometries, is getting an overhaul. Improved bindings to GEOS using fast, vectorized functions will help to substantially improve the performance of GeoPandas.

When your daily commute, shopping tour, or visit to family or friends offers many possible routes and real-time online navigation services are of no use for your individual mean of transport, you can still learn to find the optimal route with Python.

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This talk will give an overview of ongoing work on Satellite Orbits and Terrestrial Reference Frames in our Software package *Where*.

At the [GIS center](https://udg.edu/en/sigte) of the University of Girona we offer the [UNIGIS](https://unigis.es) MSc distance learning GIS programme. In the Geoinformatics specialization Python has a key role and is being taught as the main language for data manipulation and processing. In this talk I’ll focus on the role of Python in our programme and share our experience about teaching it to a variety of student profiles.

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This talk focuses on the normalization of Twitter users data collected in Istanbul, Turkey to provide a base for certain research topics such as locating emergency meeting points, detecting unexpected events, relieving traffic jams.

Maps can help you tell amazing stories with your data and a lot of datasets we come across have a location component in them. Leveraging location data to create beautiful maps was once a daunting endeavor done only by cartographers with artistic skills but now with a pinch of python and some open-source help we can easily create interactive maps with our data

For employees, academics or enthusiasts ... Maps are increasingly present in the lives of all of us. So..what about understand more how to create them? In this quick talk, we’ll see a little bit about creating a Django application and also our first WebMap using ArcGIS API for Python in a clean and nice way.

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Despite the availability of various modern deployment infrastructures - serverless, containers, servers, on-device; developers are often constrained by specific framework or programming language as governed by their firm's deployment infrastructure. This talk will focus on the emergence of various Model Exchange Formats which are democratizing the process of model development.

We show how remote sensing data, air temperature measurements and cadastral datasets can be used to simulate urban heat islands with the urban cooling model of the InVEST software platform.

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TESLA-kit (https://github.com/teslakit/teslakit) is an unique open-source software (suite of libraries) combining tools in Python and exemplified projects in the Jupyter Notebooking ecosystem, for coastal risk assessment studies.

Sen2Cube.at is a first step towards a semantic querying system for Earth observation imagery. It implements a Python-based inference engine that can be used to directly infer information from EO imagery without the need for domain knowledge or programming skills.

Statistical prediction of monthly mean sea level using a linear regression model of monthly anomalies of principal components for two regional predictors. The tool has been implemented in several tide gauge locations, and results are presented in JupyterLab notebooks.

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In this talk the project "SmartGeoAR" is presented. It is shown how deep learning is used to get location information for Augmented Reality.

Dreams about becoming a painter can be easily fulfilled with device you carry in your pocket. The story behind applying style transfer with real-time performance to the video on smartphones.

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A hybrid coastal modelling method to downscale wave climate in small Pacific islands has been developed from different Python modules. User-friendly Jupyter Notebooks are also included in the results visualization.

Hand-crafted shaded relief is an effective yet tedious method to depict terrain on topographic maps. Convolutional neural networks trained with a large set of hand-drawn examples can create imagery that is strikingly similar to expressive hand-drawn shaded relief art.

The aim of the project is to quantify the damage done to properties by hurricanes in a swift, automated and scalable way with the help of satellite imagery