AI w obserwacji Ziemi i geoinformatyce

Geospatial AI and Earth observation · id geoai · aliases: GeoAI, geospatial AI, Earth observation AI, AI for remote sensing, deep learning in remote sensing, teledetekcja z AI, sztuczna inteligencja geoprzestrzenna, AI dla danych satelitarnych

Uczenie maszynowe na danych teledetekcyjnych (optycznych, radarowych, wielospektralnych) i innych danych geoprzestrzennych: mapowanie, wykrywanie zmian, modele bazowe i walidacja przestrzenna.

Machine learning on remote-sensing data (optical, radar, multispectral) and other geospatial data: mapping, change detection, foundation models and spatial validation.

In one sentence

Komputery uczą się czytać zdjęcia z satelitów i samolotów oraz dane o miejscach, żeby na bieżąco mapować, co dzieje się na Ziemi.

Computers learn to read satellite and aerial images and location data, so they can map what is happening on Earth as it changes.

Problem it solves

Programy obserwacji Ziemi regularnie obrazują ogromne obszary, ale danych jest tyle, że żaden zespół ludzi nie zdąży ich wszystkich obejrzeć ani opisać.

Earth-observation programmes regularly image vast areas, but there is too much data for any human team to inspect and describe it all.

What it does

Jak niestrudzony kartograf, który regularnie przegląda świeże zdjęcia rozległych obszarów i dorysowuje na mapie nowe drogi, zalane pola i wycięte lasy.

Like a tireless map-maker who regularly reviews fresh images of vast areas, drawing in new roads, flooded fields and cleared forests.

Everyday example

Po powodzi ratownicy dostają mapę zalanych ulic i uszkodzonych domów, narysowaną automatycznie na podstawie świeżych zdjęć satelitarnych.

After a flood, rescuers receive a map of flooded streets and damaged homes, drawn automatically from fresh satellite images.

This concerns you if you…

korzystasz z map w telefonie i ciekawi cię, skąd są aktualne
pracujesz z ziemią, lasem albo nieruchomościami
interesują cię zmiany klimatu widziane z kosmosu
use maps on your phone and wonder how they stay current
work with farmland, forests or property
care about climate change as seen from space

Subtopics

Analiza ruchu ludzi i pojazdów (trajektorie) (Mobility and trajectory data mining): Processing and modelling sequences of positions over time: road-network matching, traffic forecasting, next-location prediction and synthetic trajectory generation.
- Dopasowanie śladu GPS do sieci dróg (Map matching): Assigning noisy, sparse position fixes to the most probable path in a road-network graph.
- Prognozowanie natężenia ruchu na sieci dróg (Traffic forecasting with spatio-temporal graph networks): Forecasting speed or flow at road sensors with networks combining graph convolution over the road network and temporal sequence modelling.
- Przewidywanie, dokąd ktoś pójdzie dalej (Next point-of-interest (POI) recommendation): Predicting the next visited place from a sequence of check-ins, using spatial, temporal and social context.
Analiza zdjęć satelitarnych i lotniczych (Satellite and aerial image analysis): Scene and land-cover classification, segmentation, object detection and change detection on optical, multispectral and radar imagery from satellites, aircraft and drones.
- Analiza obrazów radarowych (SAR) (Deep learning for SAR imagery): Deep learning on synthetic aperture radar images: terrain classification, target recognition, despeckling, interferometry and fusion with optical data.
  - Wykrywanie ruchów gruntu z interferometrii radarowej (Ground deformation detection from InSAR with deep learning): Classifying InSAR interferograms and time series with convolutional networks to detect surface deformation automatically.
  - Wykrywanie statków na obrazach radarowych (Ship and dark vessel detection in SAR imagery): Detecting and characterising vessels and offshore infrastructure in SAR imagery, cross-referenced with AIS to flag so-called dark vessels.
- Analiza zdjęć hiperspektralnych (Hyperspectral image analysis): Classifying and segmenting images with hundreds of narrow spectral bands, combining spectral and spatial features under scarce labels.
- Mapowanie pokrycia i użytkowania terenu (Land use and land cover classification): Assigning each pixel or tile of a satellite image a land-cover class (forest, water, built-up, cropland) through scene classification or semantic segmentation.
- Szeregi czasowe zdjęć satelitarnych (Satellite image time series): Classification and segmentation over stacks of multi-date images, exploiting spectral change over time, e.g. for crop-type mapping.
- Wieloklatkowa super-rozdzielczość zdjęć satelitarnych (Multi-frame super-resolution for satellite imagery): Reconstructing a higher-resolution image from multiple low-resolution revisits of the same area.
- Wydobywanie obrysów budynków ze zdjęć (Building footprint extraction from overhead imagery): Segmenting buildings in very-high-resolution overhead imagery and vectorising the masks into footprint polygons.
- Wykrywanie chmur i ich cieni na zdjęciach (Cloud and cloud shadow detection): Pixel segmentation into clear sky, thick cloud, thin cloud and cloud shadow, used as a quality mask before further optical-image analysis.
- Wykrywanie obiektów na zdjęciach z góry (Object detection in aerial and satellite imagery): Locating objects in remote-sensing images with horizontal or oriented bounding boxes (OBB), under large variation in scale, orientation and density.
- Wykrywanie zmian na zdjęciach z różnych dat (Remote sensing change detection): Detecting and classifying changes between co-registered images of one area from different dates: binary (change or not) or semantic (which class to which).
Modele bazowe dla obserwacji Ziemi (Earth observation foundation models): Models pretrained self-supervised or contrastively on large remote-sensing collections (multispectral, radar, temporal), then fine-tuned or linearly probed for mapping tasks.
- Agenci AI do analiz w systemach GIS (LLM agents for geospatial analysis (autonomous GIS)): Language-model agents that plan geoprocessing workflows, generate and execute GIS code, and visualise the results.
- Gotowe osadzenia powierzchni Ziemi (Earth embedding fields): Global annual embedding vectors for every land pixel, produced by a multi-sensor foundation model and used for k-NN classification, linear regression and change detection.
- Modele wizja–język dla zdjęć satelitarnych (Remote sensing vision-language models): Models linking a remote-sensing image encoder with a language model: contrastive (image–text retrieval) and generative (captions, VQA, box grounding).
- Samonadzorowany pretrening na zdjęciach wielospektralnych (Self-supervised pretraining for multispectral satellite imagery): Pretraining transformers on unlabelled multispectral images and time series with masked autoencoders or contrastive learning, encoding bands, dates and location.
Monitoring rolnictwa, lasów i katastrof z satelity (Satellite monitoring of agriculture, forests and disasters): Earth observation applications of machine learning: crop mapping and yield prediction, forest and biomass measurement, rapid mapping of floods and disaster damage.
- Mapowanie powodzi i szkód po katastrofach (Flood and disaster damage mapping): Segmenting water in radar and optical images and assessing building damage by comparing pre- and post-event images on a scale of damage levels.
- Mapowanie upraw i prognoza plonów (Crop type mapping and yield prediction): Classifying crop types from image time series and regressing yields from vegetation indices, weather and soil data, at field, regional or national scale.
- Pomiar wysokości koron drzew z satelity (Forest canopy height mapping): Regressing forest canopy height from multispectral imagery, trained on sparse lidar measurements and checked against independent airborne measurements.
- Prognozowanie rozprzestrzeniania pożaru na kolejny dzień (Next-day wildfire spread prediction): Forecasting the next-day fire mask from the current fire extent plus weather, terrain and vegetation data.
- Rozpoznawanie przyczyn wylesiania (Deforestation driver classification): Classifying the direct cause of forest loss from imagery around detected deforestation events; ForestNet used plantation, smallholder agriculture, grass or shrubland, and an other class.
- Szacowanie zamożności i warunków życia ze zdjęć satelitarnych (Poverty and socioeconomic mapping from satellite imagery): Regressing wealth and poverty indicators from daytime and night-time imagery, calibrated on household surveys.
- Wykrywanie wycieków metanu z satelity (Methane plume detection from satellite imagery): Segmenting point-source methane plumes in hyperspectral and multispectral imagery, reducing false detections compared with matched filters.
Uczenie maszynowe na danych przestrzennych (Spatial machine learning): Machine-learning methods that treat space explicitly: spatial validation, location encoding, modelling spatial autocorrelation and heterogeneity, and geolocalisation.
- Kodowanie położenia geograficznego (Location encoding): Mapping a point's coordinates to a high-dimensional embedding (sinusoids, spherical harmonics, networks), often trained contrastively with satellite or ground images.
- Ustalanie, gdzie zrobiono zdjęcie (Image geolocalisation): Predicting where on the planet a photo was taken: classification into geographic cells, retrieval from a location gallery, or contrastive image–GPS matching.
  - Dopasowanie zdjęcia z ulicy do mapy satelitarnej (Cross-view geo-localisation): Retrieving and metrically localising a ground-level image by matching it against georeferenced aerial or satellite imagery.
- Uzupełnianie mapy między punktami pomiarowymi (Spatial interpolation with machine learning and geostatistics): Predicting a continuous variable at unsampled locations via kriging, machine-learning models with spatial features, or a combination.
- Walidacja przestrzenna (bloki zamiast losowania) (Spatial cross-validation): Splitting data into test folds by spatial blocks, buffers or distance to estimate model error under spatial autocorrelation and extrapolation.

Sources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources · Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
Foundation Models for Remote Sensing and Earth Observation: A Survey · Aoran Xiao, Weihao Xuan, Junjue Wang, Jiaxing Huang, Dacheng Tao, Shijian Lu, Naoto Yokoya
Handbook of Geospatial Artificial Intelligence · Song Gao, Yingjie Hu, Wenwen Li (red.)
GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond · Krzysztof Janowicz, Song Gao, Grant McKenzie, Yingjie Hu, Budhendra Bhaduri
TorchGeo: Deep Learning With Geospatial Data · Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee