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Обычная версия сайта
Neuroinformatics and Semantic Representations: Theory and Applications

Cambridge Scholars Publishing, 2020.

Глава в книге
A comparative approach to nominal morphology in Transeurasian: Case and plurality

Gruntov I., Mazo O.

In bk.: The Oxford guide for the Transeurasian languages.. Oxford: Oxford University Press, 2020. Ch. 31. P. 522-553.

Темпоральные расширения в стандарте SQL

С.Д. Кузнецов

Препринты ИСП РАН. Институт системного программирования им. В.П. Иванникова РАН, 2017. № 30.

Разработка геоприложений (на английском языке)

Geoapplications development

This course gives programming experience with geospatial data as well as related theory and algorithms. We will use Java and JavaScript to work with diverse libraries, spatial databases and servers to store, process, visualize and exchange geodata. The course is beneficial for any modern software developer due to explosive growth in popularity of geo-aware applications and services.

The knowledge from this course is greatly important in a broad range of industries operating with geodata:
— environmental monitoring
— transportation
— urban planning
— agriculture
— insurance
— real estate sector
and many other practically important fields.

Examples include: Apple, Roscosmos, Forest Watch, Planet, Croc, DigitalGlobe, ESRI, Carto, and many others.

The course covers topics from a complete stack of geospatial technologies, including:
— fundamentals: coordinate systems, cartographic projections, industry standards, GPS, geocoding, geohash algorithm, vector and raster data types and operations
— I/O with vector KML, GeoJSON (doc1, doc2), WKT and raster GeoTIFF, NetCDF, HDF formats
— network protocols for geodata exchange: WCS, WFS, WMS, OPeNDAP
— frameworks: Mina, Netty that is used by Twitter, Yandex, Facebook, RedHat, and other companies
— spatial databases and servers: PostGIS, RasDaMan, GeoServer, TDS
— software libraries for geodata processing: vector GeoTools, Java Topology Suite and raster ImageMagic, GDAL
— processing Landsat and Terra satellite imagery (tiling, pyramid, color enhancement)
— visualizing geodata with NASA WorldWind, Leaflet.js, Mapbox

Prerequisites: confident programming skills in Java. Please, be ready for programming assignments and reserve disk space on your laptop/home PC (approx. 5 GB) for data and software that will be used during the course.

The course site rgeo.wikience.org contains for the previous year (2017, fall) all code, geodata, PowerPoint slides, exam questions, data surveys, links to software, and the syllabus.