April 22nd - April 23rd 2021

website - https://event.geopython.net/#/room/#announce:event.geopython.net

schedule - https://2021.geopython.net/schedule.html

Room 1 videos - https://vimeo.com/user/137335150/folder/4121205

Room 2 videos - https://vimeo.com/user/137335150/folder/4250501

Tutorials

Interactive mapping and analysis of geospatial big data using geemap and Google Earth Engine, Qiusheng Wu

https://geemap.org/workshops/GeoPython_2021/

Geopsatial analysis using python 2021, Krishna Lodha, SEGES

https://github.com/krishnaglodha/geopython2021-Geospatial-analysis-101

Talks

Day 1 room 1

Geopythonic processing of massive high resolution Copernicus Sentinel data streams on cloud infrastructure. Konstantinos Anastasiakos, Guido Lemoine, JRC D5 - GTCAP Team

https://github.com/ec-jrc/cbm

https://jrc-cbm.readthedocs.io/en/latest/

Confirm or reject farmer declares practice (markers)

Sentinal-2 is optical, Sentinal-1 is microwave

Used rasterstats previously (mean, std, min, max, p25, p50, p75) for the bands of the images. Expensive as does raster -> vector. Now speed up as a look up using numba.

https://creodias.eu/price-list has pricing of services

Interpolating Elevation Data inside Tunnel and Bridge Networks, Alexander Held, geops

https://geops.ch/en

https://geops.github.io/geops-routing-demo/?mot=rail&resolve-hops=false&x=949042.143189&y=5899715.591163&z=6

NetworkX, numpy solution

Q. asked more high resolution terrain data: https://geoservice.dlr.de/web/dataguide/tdm90/

How to use geosocial data to identify CGP Demand Hotspots, Argyrios Kyrgiazos, carto

Carto offer geospatial data https://carto.com/spatial-data-catalog/browser/

Mapquadlib, Christian Stade-Schuldt, here

https://res.cloudinary.com/here/image/upload/v1619085466/maps/HereQuadWorldLvl5_mzsg7x.png

https://res.cloudinary.com/here/image/upload/v1619085466/maps/MercatorQuadWorldLvl5_wjwbds.png

to be released

comparisons to https://h3geo.org/

30 maps in 30 days, Alexander Kmoch

https://github.com/allixender/30MapChallenge2020

https://allixender.github.io/30MapChallenge2020/geopython2021/index.html

https://allixender.github.io/30MapChallenge2020/

idea from https://tjukanov.org/

geospatial data catalog - https://data.world/

https://hackmd.io/9QHsCvq9SeyhUlrFW0fspA - conference chat on open geospatial datasets

Python in QGIS, Zoltan Siki

Predicting Traffic Accident Hotspots with spatial data science, Miguel Alvarez, CARTO

https://github.com/CartoDB/cartoframes

https://carto.com/blog/predicting-traffic-accident-hotspots-with-spatial-data-science/

https://public.carto.com/kuviz/3672c7a5-2e83-45a0-9727-ebab9596ecc5

Data: Accidents; Traffic signs. OSM: street inspections, building footprints; AEMET - daily weather

Premium data: TomTom (road network, traffic density). POIs (points of interset) from Pitney Bowes, 13 categories

Human mobility: Vodafone. 250 x 250 m grid. Working population: Unica360

https://carto.com/spatial-data-catalog/

Moran I for classification of areas hot vs cold.

used https://pysal.org/libpysal/ for weights https://pysal.org/libpysal/api.html https://pysal.org/libpysal/generated/libpysal.weights.lag_spatial.html#libpysal-weights-lag-spatial -

https://pysal.org/libpysal/api.html#sphere

Feature engineering: spatial lags (distance from city center; how close intersections are)

indices: types of roads

https://pysal.org/tobler/api.html for some interpolation

https://mmaelicke.github.io/scikit-gstat/index.html for ?

https://github.com/GeoStat-Framework/PyKrige used for regression kriging (krigging on the residuals of RF)

Django, Marc Compte

QGIS Bridge, Metadata & Geostyler, Paul van Genuchten, geocat

Seismic silences during the COVID19 lockdown, Artash Nath

http://monitormylockdown.com/

https://github.com/Artash-N/Monitor-My-Lockdown-Using-Seismic-Vibrations

SaferPLACES platform: a Geopython-based climate service addressing urban flooding hazard and risk, Stefano Bagli

https://saferplaces.co/

http://platform.saferplaces.co/

https://earthengine.google.com/timelapse/ - for motivation of sprawling cities

Flood hazard model. pluvial - extreme rainfall event. safer_rain - hierarchical filling and spilling

Coastal extreme sea levels. safer_coast

Riverine flood. fluvial.

Damage assessment bayesian network model

Mitigation measures

https://samapriya.github.io/awesome-gee-community-datasets/projects/global_power/

ML-Enabler: Enabling Rapid Machine Learning Inference of School Mapping in Asia, Africa and South America, Martha Morrissey, Development seed

https://github.com/developmentseed/ml-enabler

https://minds-behind-maps.simplecast.com/episodes/ep-1-ian-schuler-building-an-impact-driver-geospatial-company

Image classification of tiles (1 = school. 0 = no school).

TT-SNE shows mix of images.

Use https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset for effective TF training. Use Kubeflow.

Day 1 room 2

Crop yield prognosis using ML and EO data, Peter Fogh, SEGES

https://vimeo.com/geopython/review/539850665/a5a1e8d8d7?sort=lastUserActionEventDate&direction=desc

https://www.seges.dk/software/plante/cropmanager

https://en.seges.dk/

Used gradient boosting from sklearn

Interpolate to daily data and resample to 14 day data

Mapping, Monitoring and Forecasting Groundwater Floods in Ireland, Joan Campanyà i Llovet, IT Carlow / Geological Survey Ireland

https://vimeo.com/geopython/review/539470731/ef072137e0?sort=lastUserActionEventDate&direction=desc

The power of "Where" - Location data in Moovit, Yehuda Horn

https://vimeo.com/geopython/review/539470769/566a70124c?sort=lastUserActionEventDate&direction=desc

https://moovit.com/

https://github.com/mrcagney/gtfs_kit

Universal geospatial data storage with TileDB: No more file formats, Norman Barker, TileDB Inc

https://vimeo.com/geopython/review/539850335/470f80d8bc?sort=lastUserActionEventDate&direction=desc

https://github.com/TileDB-Inc/TileDB-Py

https://github.com/TileDB-Inc/TileDB-Examples

https://docs.tiledb.com/main/

https://www.capellaspace.com/community/ - SAR data

3D Geological Modelling using GemPy, Kristiaan Joseph

https://vimeo.com/geopython/review/539073526/bfb74ae414?sort=lastUserActionEventDate&direction=desc

The Open Data Cube (ODC): a very intuitive tool to store, manage and analyze satellite images data, Aurelio Vivas

https://vimeo.com/geopython/review/539470798/d7a8664c57?sort=lastUserActionEventDate&direction=desc

https://github.com/DonAurelio/geopython-2021

https://github.com/DonAurelio/geopython-2021/blob/main/Presentation.pdf

https://datacube-core.readthedocs.io/en/latest/index.html

eemont: A Python Package that extends Google Earth Engine, David Montero Loaiza

https://vimeo.com/geopython/review/539470855/1d71f86f58?sort=lastUserActionEventDate&direction=desc

https://github.com/davemlz/eemont

https://eemont.readthedocs.io/en/0.1.9/

Nice methods e.g. ee.ImageCollection().maskClouds().filterBounds().scale().index()

https://github.com/davemlz/awesome-ee-spectral-indices

https://earthengine-stac.storage.googleapis.com/catalog/catalog.json

Deep learning-based remote sensing for disaster relief with Python, Thomas Chen

https://vimeo.com/geopython/review/540097666/ff29681003?sort=lastUserActionEventDate&direction=desc

https://xview2.org/

ResNet18 - petrained on image net.

Cross-entropy loss

ADAM

LR = 0.001

100 epochs

K80 GPU

Gradient class activation map

Day 2 room 1

Predicting dissolved oxygen in a lagoon using interpretable machine learning, Dimitris Politikos

Extremely Randomized Trees (ERT)

The Mission Support System, Reimar Bauer

A QT application, a OGC web map server, a collaboration server to plan atmospheric research flights

https://github.com/Open-MSS/MSS

https://gmd.copernicus.org/articles/5/55/2012/gmd-5-55-2012.pdf

Curie Point Depth Mapping using PyCurious from Aeromagnetic Data, Izzul Qudsi

https://brmather.github.io/pycurious/

Understanding Qiskit: Quantum by Quantum, Anmol Krishan Sachdeva

https://github.com/greatdevaks/geopython-qiskit

https://github.com/greatdevaks/geopython-qiskit/blob/main/Understanding%20Qiskit%20Quantum%20by%20Quantum%20-%20Anmol%20Krishan%20Sachdeva%20GeoPython%202021.pdf

https://qiskit.org/

Improved Crop Yield Prediction through Spatio-Temporal Analysis of Agricultural Data, Arjumand Younus

https://docs.google.com/presentation/d/1gqobnB-nDUmych2isMhEjq1caZEpIub5YX9Z8krPXdQ/edit#slide=id.p

https://github.com/phanein/deepwalk

The Bavarian Open Data Cube. Sebastian Foertsch

https://github.com/opendatacube?type=source

The Participatory Terrain model (ParTerra) in Python, Arjen Haag, Deltares

https://drive.google.com/file/d/1dD62ygkgrW4NrYou_I8o4X90YQiQl2Qa/view?usp=sharing

https://gitlab.com/deltares/parterra/parterra-python

OSM - https://www.youtube.com/watch?v=7sC83j6vzjo&t=27s

Over-Simplified Modeling - The case of the Global Warming Potential. Mike Müller

FaIR: Finite Amplitude Impulse Response simple climate model

https://github.com/OMS-NetZero/FAIR

https://github.com/hydrocomputing/igwp/tree/master/

https://www.nature.com/articles/s41612-019-0086-4

https://gitlab.ouce.ox.ac.uk/OMP_climate_pollutants/co2-warming-equivalence/

Trackintel: An open-source python library for human mobility modeling and analysis, Ye Hong

https://github.com/mie-lab/trackintel other packages in this space https://github.com/anitagraser/movingpandas https://github.com/scikit-mobility/scikit-mobility

this focusses on human mobility

https://blog.mapbox.com/9-years-of-openstreetmap-gps-tracks-available-for-mapping-47f6074c0688

https://www.microsoft.com/en-us/download/details.aspx?id=52367

On the role of packaging in GIS, or: How to drive computer clusters and GPUs from within desktop GUI applications for non-technical users, Sebastian M. Ernst

https://github.com/qgis/QGIS-Enhancement-Proposals/issues/179

Pyinterpolate - Python package for spatial interpolation and deconvolution of areal data, Szymon Moliński

https://github.com/szymon-datalions/pyinterpolate

https://pyinterpolate.readthedocs.io/en/latest/tutorials.html

Bins! An easy path to make them using Fast API, PostGIS and JavaScript. Vinícius Cruvinel Rêgo

Spatial SQL? ...can you say that in Python, please?, César Ariel Pérez Mercado

Audio Signal Processing for Feature Building and Machine Learning, Jyotika Singh

https://drive.google.com/file/d/1xdUbVbzrVcaKXu1CMIk4I5whFk3gD8s1/view

https://github.com/jsingh811/pyAudioProcessing

Cal ToxTrack: A Web GIS for Pollution Mapping in California, Megan Luisa White

https://docs.djangoproject.com/en/3.2/ref/contrib/gis/#module-django.contrib.gis

use PostgreSQL db with PostGIS

https://www.youtube.com/watch?v=F5mRW0jo-U4

Day 2 room 2

Travel Time Prediction for Urban Travel using Uber Movement and OpenStreetMap, Vishnu Prasad

https://spatialthoughts.com/2020/02/22/snap-to-roads-qgis-and-osrm/

https://github.com/spatialthoughts/spatial-data-science/tree/master/travel_time_prediction/data/googlemaps

https://travel-time-prediction-app.herokuapp.com/

http://project-osrm.org/

Spatial analysis of Covid-19 relation with weather parameters, Abouzar Ramezani

Maps with Django, Paolo Melchiorre

https://www.paulox.net/2021/04/23/geopython-2021/

https://docs.djangoproject.com/en/3.2/ref/contrib/gis/db-api/#creating-and-saving-models-with-raster-fields

Exploratory Movement Data Analysis, Anita Graser

https://vimeo.com/geopython/review/539472075/d68aff3d10?sort=lastUserActionEventDate&direction=desc

https://anitagraser.github.io/movingpandas/

Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum ‒ Journal of Geographic Information Science 2019, 1-2019, 54-68. doi:10.1553/giscience2019_01_s54.

https://github.com/anitagraser/EDA-protocol-movement-data

https://fosdem.org/2021/schedule/event/geopandasholoviews/

https://www.youtube.com/watch?v=dRE9Zl7jpUA

https://github.com/anitagraser/movingpandas-examples

Should We Return to Python 2?, Miroslav Šedivý