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Archive of posts filed under the BigData category.

H2020 AI and Big Data

Big Data and Artificial Intelligence for Earth Observation (EO) 19-20 November 2020, European Commission workshop The two-day workshop presented nine research projects on big data and artificial intelligence for Copernicus and Earth Observation, funded under the Horizon 2020 Space Programme. European Union’s Earth Observation Programme The results of the five projects selected under the topic […]

Buzzwords on Advanced Methods

On the OSGeoJapan-discuss mailing list regarding FOSS4G 2019 KOBE.KANSAI : 今年は、Geo-AIのテーマにマッチした、ディープラーニングの勉強が初歩から体験できるコースが2つあります!   translation: this year, there are two courses that match the theme of Geo-AI, where you can experience deep learning from the beginning! A OSGeoJapan wiki page says:    今年のテーマは「Geo-AI」です。     translation: this year’s theme is “Geo-AI”. Geo-AI a closer look – “geo” […]

AGU Goes to NOL

best to all in the next week e.g. Oak Ridge National Labs research group LINK


The Group On Earth Observation System of Systems (GEOSS) plenary conference was held in November of 2016. 2017 Work Plan -LINK- Earth System Grid -PAGE-  

AmpCamp 2014

BDAS  the Berkeley Data Analytics Stack At a minimum, suffice it to say I participated online in roughly twelve hours of lecture and lab on Nov 20 and 21, 2014 at AmpCamp 5 (I also attended one in Fall 2012). I put an emphasis on python, IPython Notebook, and SQL. Once again this year, the […]

Numeric Stats on Bay Area Intersection Counts

In preparing for an upcoming Datathon, a column of data in PostgreSQL numeric format needed formatting for presentation. “Intersection Count” intersection_density_sqkm is a count of street intersections per unit area – a quick way to measure density of the built environment. A table of grid cells (covering the nine-county San Francisco Bay Area) that the […]

Worldwide Forestry Inventory Published, Nov13

Dozens of major news outlets posted articles yesterday profiling a paper published in the journal ‘Science’ by a team led by Matthew Hansen, a remote sensing scientist at the University of Maryland, along with extensive data. ‘Published by Hansen, Potapov, Moore, Hancher et al. * Powered by Google Earth Engine‘

California POIs 2013

I took a short course on the US Census at the University of California, Berkeley D-Lab recently. Of course, the first topic was the shutdown of People using the convenient, interactive Census API have been left without access to census data. Two hours of condensed lecture was just enough time to cover the basics […]

Five Colors for Stats

I am building some visualization layers in Geoserver from PostGIS, which requires .sld files (until Geoserver catches up with the CSS styling world – oh wait, look here). It is convenient to show ranges using ColorBrewer2 colors in a set of one plus five.. a color for NoValue, then what I call little0, little1, central […]

PostgreSQL 9.3 plus Hadoop File System

It seems that things just got a little bit more interesting with the release of Pg 9.3