The future of (Developer) Tooling for Geospatial +
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+ María Arias de Reyna Domínguez
+@delawen@floss.social - @delawen@twitter.com
+Senior Software Engineer
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+ Who am I?
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+ What we are going to talk about
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+ FAIR Data and Reproducible Science
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+ The Evolution of Geospatial
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+ Analogic Era
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+ Digitalization
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+ Location Intelligence
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+ Smart Geospatial
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+ Food for Thought
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+ Questions?
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+ -
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- 2008 Software Engineer +
- Free and Open Source Advocate +
- GeoSpatial Ranger +
- 2007 Web Mapping Artificer +
- 2009 Dijkstra A* Warlock +
- 2012 Metadata Paladin +
- 2013 OSGeo Charter Member +
- 2017-2019 OSGeo President +
- Integration Druid +
- 2019 Apache Camel contributor +
- 2020 Apache Software Foundation Committer +
- 2021 Create Low Code/No Code Integrator +
- Java Champion +
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+ Developer Tooling for GeoSpatial
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- What do we mean by Tooling? +
- Guess the future +
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+ What do we want?
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+ When we talk about GeoSpatial Science
+What do we want?
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- FAIR Data +
- Findable +
- Accessible +
- Interoperable +
- Reusable +
- Reproducible Science +
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How to be FAIR
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- Use Standard Formats +
- Use Standard Protocols +
- Like INSPIRE +
- Publish Data with Open License +
- Use Open Data +
How to Reproduce Science
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- Hand by hand with FOSS +
- Need to see and run the code +
- Not all scientists are developers +
- Reuse libraries and frameworks +
- The less code, the better +
- Low Code and No Code +
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How Software Evolves
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+ Does this also apply in GeoSpatial?
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+ Evolution of Geospatial
+The best way to infer our future is to study our evolution.
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- Analogic +
- Digitalization +
- Location Intelligence +
- Smart Geospatial +
Analogic Era
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+ Mappa di Eratostene
+Analogic Era
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+ Census Bureau cartographers prepare maps for the 1960 census.
+Digitalization
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- Better Editing Tools +
- Better Storage Formats +
- More Detail +
- GeoComputation +
- Ontology and Semantics +
- Able to manage more Datasets +
- Satellite and Aerial Data +
- Raster!! +
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Which leads to high quality Big Data
+The power of Big Data
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- Python +
- R +
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- GDAL/OGR +
- QGIS(2002!) +
- GRASS GIS(1982!) +
- GeoTools +
- Orfeo Toolbox +
What do we do with the Big Data?
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+ The power of Big Data
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- Artificial Neural Networks (ANN) +
- Heuristic Algorithms +
- Fuzzy Logics +
- Deep Learning +
The power of Big Data
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- Identifying Spatial Patterns +
- Singularities and Anomalies +
- Land Use +
- Detect illegal buildings +
- Spatial Interpolation and Prediction +
- Flooding Prevention and Mitigation +
- Coastline evolution +
- Exploring Spatial Factors +
- Spatial Simulation +
- Decision Making +
- Better urban planning (roads, facilities,... ) +
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Location Intelligence
+Business Intelligence for GeoSpatial
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+ Frameworks to simplify the work
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+ //Start with a timer that executes the operation every 10 seconds
+ from("timer:java?period=10000")
+ //Access the CSV file which can be on an api or storage device
+ .to("{{source.csv}}")
+ //unmarshal and split the workflow per row
+ .unmarshal().split(body()).streaming()
+ //process each row
+ //the process is defined in a separated file
+ .process(processCsv)
+ //push the results to the following API
+ .to("https://nominatim.example.com/reverse")
+ //the returned XML is also processed
+ .unmarshal().jacksonxml()
+ //again, the processor is defined in a separated file
+ .process(processXML)
+ //Prepare an SQL query based on the result of the process
+ .setBody().simple("SELECT info FROM table WHERE id like '${exchangeProperty.pollutant}'")
+ //send the SQL query to the database
+ .to("jdbc:postgresBean?readSize=1")
+ //collect the result and process it
+ .process(processDB)
+ //reunite the parallel streams that started on the csv processing
+ .aggregate(constant(true), aggregationStrategy)
+ .completionSize(5)
+ //given the list of outputs per row in csv, process it
+ .process(buildGeoJSON)
+ //store the result in another database
+ .to("mongodb:mongoBean?database=example&collection=mySpatialObjects&operation=insert")
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+ Smart Geospatial
+When we met "Artificial Intelligence"
+What we won't discuss
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+ Let's focus on what we can, not what we should.
+Artificial Intelligence
+Natural Language => Structured Format
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- Natural Language -> Query +
- Specific LLM models +
- Natural Language -> Spatial Data +
- Semantic models +
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Raster => Structured Data
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- Imagine Processing +
- Blind descriptions +
Structured Format => Natural Language
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- Generate map descriptions +
- Generate labels automatically +
- Choose the best style +
Example Case 1
+ +Generate a map based on locations described in a text.
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+ Example Case 2
+ +Instantly turn text sources and ChatGPT prompts into insightful interactive geo maps.
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+ Example Case 3
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+ Are we there yet?
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+ Generative AI vs Derivative AI
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Ethical concerns
+ - Security concerns +
- Where does my data go? +
- Am I training my competitors? +
- Whose data I'm using? +
- Mix different types of AI +
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There's a long road ahead
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- AI Helpers +
- Higher programming languages +
- Low Code +
- No Code +
- Better Frameworks and Libraries +
- Reuse as much code as you can +
- Validate your Data Sources +
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Questions?
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