Análisis de cambios con SAR (Radar de Apertura Sintética) usando Sentinel-1 sobre la mina Bingham Canyon en Utah, donde ocurrió uno de los mayores deslizamientos de tierra de la historia minera el 10 de abril de 2013.
Category Archives: clutter
SolarScope: cuando el catastro, el LiDAR y el sol se sientan a la misma mesa
Llevo unos días dándole vueltas a una idea que, en el fondo, es bastante sencilla: si tenemos la huella de cada edificio, su altura y un modelo digital de superficies de alta resolución, ¿por qué seguimos viendo estudios de potencial solar que tratan los tejados como manchas homogéneas sobre un mapa? De esa pregunta, y de unas cuantas sesiones intensas de R, ha salido SolarScope, una aplicación Shiny que estoy desarrollando para hacer scoring de potencial fotovoltaico tejado a tejado, con datos abiertos y un flujo que se puede reproducir tanto en España como en Estados Unidos.
Time Series Analyzer: Análisis multitemporal de índices espectrales en Google Earth Engine
He desarrollado una aplicación interactiva en Google Earth Engine para la extracción y análisis estadístico automático de series temporales de cinco índices espectrales (NDVI, EVI, SAVI, NDWI y NBR) sobre cualquier geometría definida por el usuario en cualquier sitio del mundo. El objetivo es pasar de una imagen satélite puntual a una comprensión temporal del territorio: qué ha pasado, qué patrón subyace, y qué cabe esperar.
POI Intelligence for Urban Asset Analysis in RStudio: assetIQ
When analysing urban assets, there is genuine value in moving beyond generic neighborhood scores. The density of a coffee shop cluster, the proximity to a financial hub, or the concentration of accommodation around a transport node are signals that traditional datasets flatten into averages — or ignore entirely. assetIQ was built to change that. assetIQ is an R application powered by DuckDB and Overture Maps that extracts, classifies, and scores Points of Interest (POIs) for any location on Earth. You define a city and a search radius — from 100 meters to 25 kilometers — and the tool queries the Overture Maps Places dataset in real time, classifying each POI into thematic groups: Food & Drink, Retail, Health, Education, Transport, Accommodation, Financial Services, Leisure & Culture, Sport, and more.
The core output is an attribute value called POIQ: a normalized 0–1 score assigned to every building footprint within the area of interest, derived from a Kernel Density Estimation of the selected thematic group. A building in a dense retail corridor scores close to 1. An isolated residential block far from any commerce scores close to 0. This transforms thousands of individual points — which in raw form tell you very little — into a single, interpretable attribute per building, ready for downstream modelling, valuation, or site selection.
CHANGE DETECTION ARCGIS PRO AND LIVING ATLAS 2017-2025
The quantification of land-use dynamics necessitates a spatiotemporal framework that ensures categorical stability over long-term observation windows. The ESRI 10-Meter Global Land Cover time series, accessible through the ArcGIS Living Atlas, provides a harmonized baseline for this purpose, derived from the dense temporal stack of the ESA Sentinel-2 mission.
Who Gets to See the War? Satellite Imagery Censorship and the Copernicus Alternative
A private company, operating under a US federal license, was effectively told by the Trump administration to go dark over an active war zone. Planet complied. Vantor and BlackSky — both heavily dependent on US defense revenue — said they hadn’t even received such a request, because they were already tightly controlling access. The selective pressure lands precisely on the most open, most commercially accessible provider. That is not a coincidence.A private company, operating under a US federal license, was effectively told by the Trump administration to go dark over an active war zone. Planet complied. Vantor and BlackSky — both heavily dependent on US defense revenue — said they hadn’t even received such a request, because they were already tightly controlling access. The selective pressure lands precisely on the most open, most commercially accessible provider. That is not a coincidence.
From Overture Maps to GPKG in minutes: Building a Geospatial Data Extractor with R and DuckDB
Modern geospatial workflows increasingly depend on fast, reliable access to city-scale vector data — building footprints, road networks, land use polygons, points of interest, address databases. Whether you are designing a 5G radio network, modelling urban heat islands, planning last-mile logistics, or simulating emergency response coverage, you almost always start from the same question: “How do I get clean, structured geodata for this city, right now, without spending two days on it?”
The Overture Maps Extractor is my answer to that question. It is a Shiny application written in R that lets any GIS professional extract multiple thematic layers from the Overture Maps Foundation dataset — for any city in the world — in a matter of minutes, with zero command-line interaction and zero manual data wrangling.
Urban development in Madrid from the mid-19th century to the present day
All existing buildings in Madrid currently listed in the Land Registry database have their year of construction recorded. This map shows, by decade, where the bulk of that urban development took place. For example, in the 1920s it was in the Salamanca district, in the 1930s in Chamartín… shifting from development in the city centre to the outskirts.
Population Estimation through Dynamic LULC-Based Settlement Validation
The foundational step of this methodology involves the deployment of a centralized processing interface within the Google Earth Engine (GEE) environment. The provided visualization captures the core interface of the custom GEE application, which serves as the hub for the multi-sensor LULC validation pipeline. Within this dashboard, users can define a specific Area of Interest (AOI)—highlighted here over the Iberian Peninsula and North Africa—and configure key parameters, including temporal ranges for the acquisition of sentinel-derived products. Crucially, the interface is designed to load and compare two primary datasets simultaneously: Dynamic World (near real-time, probability-based LULC) and ESA WorldCover (10m resolution structured LULC). The contrasting classification schemes are represented by the legends on the left and right sides of the map view, which illustrate the varying definitions of ‘Built-up’ and urban areas between the two products. Establishing this visual and statistical comparison at the application level is the prerequisite for calculating the spatial disagreement threshold, or delta, that guides the subsequent merging and population estimation phases.
Agricultura de precisión (II). APP para integración con Catastro rural en España
La convergencia entre el Big Data geoespacial y la administración pública ofrece una oportunidad sin precedentes para la optimización agronómica. La capacidad de procesamiento de Google Earth Engine (GEE), vinculada a la cartografía vectorial del Catastro rural, permite transformar las series temporales de misiones como Sentinel-2 en herramientas de diagnóstico directo sobre la parcela. Este enfoque desplaza el análisis de una observación puramente visual a una monitorización cuantitativa basada en la respuesta espectral de los cultivos. El núcleo de esta aplicación reside en la intersección geométrica de las parcelas catastrales con colecciones de imágenes multiespectrales. Mediante el uso de la API de JavaScript en GEE, se automatiza el cálculo de indicadores biofísicos críticos como el NDVI (Índice de Vegetación de Diferencia Normalizada), el NDWI (Índice de Agua de Diferencia Normalizada), el EVI (Índice de Vegetación Mejorado) y el SAVI (Índice de Vegetación Ajustado al Suelo). Estos índices no solo reflejan el vigor fotosintético, sino que permiten identificar anomalías de crecimiento, estrés hídrico o variaciones en la densidad foliar que son invisibles al ojo humano en las fases tempranas del ciclo fenológico.
Geovisualization.net 