Google Earth Engine and Dynamic World

Let me please introduce you this “new” LULC source I have come across with recently. The potential of this 10m “clutter” source is being able to acquire data from a few days ago instead of using outdated “very old” 2020 vintage datasets. I know if these days something 2020 is very old then myself, born in 1972 then i’m older than the riverside, older than peeing in a wall, even older than Methuselah. Yes, that’s the way it is nowadays.

Google Earth Engine is a geospatial processing service where you can perform geospatial processing at scale, powered by Google Cloud Platform. The purpose of Earth Engine is to:

Provide an interactive platform for geospatial algorithm development at scale
Enable high-impact, data-driven science
Make substantive progress on global challenges that involve large geospatial datasets

Wildfires in the sub-Saharan region

This image, acquired by one of the Copernicus Sentinel-2 on 8 February 2022, shows ongoing wildfires in the Boma National Park in South Sudan. Fires are common at this time of year in the sub-Saharan region.

Allocation analysis: Attaching customers to facilities

Allocates a set of demand points (Customers) to user specified number of supply points (Facilities) out of a Facilities point dataset based on the Euclidian distance between the Customers and Facilities.

Réalisation du carte de densité pour vérifier Localisation des colonnes aériennes de Nantes Métropole

Localisation et caractéristiques des colonnes d’apport volontaire aériennes de Nantes Métropole utilisées pour la collecte des déchets. Outil de visualisation Global Mapper 17 Format SHP champ: VOLUME Ces colonnes sont implantées sur l’ensemble du territoire et sont destinées à la collecte du verre et des emballages recyclables (papier, carton, plastique). C’est genial jouer unContinue reading “Réalisation du carte de densité pour vérifier Localisation des colonnes aériennes de Nantes Métropole”

Entrevista en ‘Soy Data’

Me vinieron a entrevistar de  para hablar de temas relacionados con la Geovisualización como el Big Data o el Open Data y su implicación con el Control de Calidad o el Software libre. Gracias a Jorge Ubero de SoyData por la misma. Espero que os guste, ya sabéis que si tenéis algún comentario o algoContinue reading “Entrevista en ‘Soy Data’”

Creating value through Open Data

The benefits of Open Data are diverse and range from improved efficiency of public administrations, economic growth in the private sector to wider social welfare (Source: Performance can be enhanced by Open Data and contribute to improving the efficiency of public services. Greater efficiency in processes and delivery of public services can be achieved thanks toContinue reading “Creating value through Open Data”

Comparación de DTM usando Global Mapper 17.0.1

Comparar, primero visualmente y después cuantitativamente dos DTM. Por un lado elegimos una fuente muy usual, SRTM con un DTM derivado de Fotogrametría Stereo.

Descargas del CNIG. Open Source bien hecho!

Hola amigos del GIS, Por motivos de trabajo que no vienen al caso, he tenido que bucear de manera sistemática la web de descargas del CNIG. Una maravilla. Por motivos que tampoco viene al caso, he de hacer esto mismo de vez en cuando en todos los Institutos cartográficos del mundo y el delContinue reading “Descargas del CNIG. Open Source bien hecho!”

Change detection – Detección de cambios en polígonos

THE IDEA: DEMONSTRATING HOW DYNAMIC A CITY IS, THUS HOW IMPORTANT IS HAVING AN UPDATED DATASET THE FACTS: THE CITY OF BOGOTÁ IN COLOMBIA 2012-2014 Overall growth rate: -0.12% ONLY HAVING INTO ACCOUNT THE DIFFERENCE OF BUILDINGS CAPTURED BETWEEN 2012 AND 2014 (We can do this because we have used the same data capture model in both years) (DeContinue reading “Change detection – Detección de cambios en polígonos”

DTM validation using Google Earth (and RMSE extraction)

Hi guys, Surfing the internet is great when you need to figure out something. I needed to validate some DTM from unknown sources against an also unknown source (but at least a kind of reliable one, Google Earth). All we need is Google Earth TCX converter ARcGIS Excel This is the procedure i have followed:Continue reading “DTM validation using Google Earth (and RMSE extraction)”