Live and recent posts from across the Fediverse tagged #googleearthengine, aggregated by home.social.
Why does a hillshade light the land from the northwest at 45 degrees? Cartographers have used it since the 1950s because light from that direction stops ridges looking like valleys.
Lay slope over the colour infrared at half opacity and you get a two and a half D view without leaving the browser.
Mean tells you how green an area is on average. Change one word to standard deviation and the same block tells you how mixed it is.
Rename the label too, or you will be reading the wrong number.
Loading aerial imagery in Earth Engine: filter by place first, then by date, then mosaic.
The place filter throws away every tile that does not touch your area before anything else runs. That order is what makes the rest of the script cheap.
One word. Change mean to stdDev in a seventy line script and Earth Engine tells you something completely different about the same patch of ground.
Why take drone work into code at all? If someone has to rerun your analysis a year from now, a script is a far better handover than a sequence of clicks.
【🎉Latest accepted article】
Decoding nearly four decades of #rubberplantation age in China using #Bi_directionalCCDC and height–age model
#GoogleEarthEngine | #Handheldlaserscanning | #Standage | #Temporaldynamics
Every year, at least one person gets these the wrong way round and wonders why their study area is in the Indian Ocean.
In Google Earth Engine, a point is longitude first, then latitude.
Then three filters: bounds, date and cloud. Where, when and what, the same three questions the browser asked.
#GoogleEarthEngine #RemoteSensing #GIS #Coding #Geography #EarthObservation
Twenty minutes of pointing and clicking found three satellite scenes.
Every Sentinel-2 scene over this area for the last five years? I'm not clicking through that. I'm certainly not downloading it.
Google Earth Engine asks the same three questions, where, when and what, as code instead of clicks. The data never leaves Google's side.
#GoogleEarthEngine #RemoteSensing #GIS #Sentinel2 #Geography #EarthObservation
Six bands, divided by 10,000, to turn a stored integer back into reflectance.
Sampling Sentinel-2 in Google Earth Engine, drawing tools for the points rather than code, then exporting straight to CSV.
The same surfaces, this time straight from Sentinel-2.
🛰️ On September 28, I will be presenting at Data for Good (Calgary) on “Satellite Remote Sensing & Open Data: From Earth Observation to Measurable Public Impact in Calgary”.
💻 The talk will focus on the technical workflow: ingesting and processing multi-sensor Sentinel and Landsat imagery using Google Earth Engine and R, machine learning for land cover classification, and modeling neighborhood-scale land surface temperature (LST) patterns across the city.
🌳 We will also discuss the role of spatial data science and data visualization in informing local urban climate resilience and municipal open data initiatives. Looking forward to the technical discussion, Q&A, and connecting with Calgary’s data and geospatial community.
🔗 Registration link is here:
https://meetu.ps/e/QgbbR/ZtysN/i
#DataForGoodYYC #Geospatial #RemoteSensing #RStats #GoogleEarthEngine #OpenData #Calgary #EnvironmentalData #GreennessOfCalgary
Research on the spatiotemporal evolution characteristics of NDVI in Shendong mining area based on GEE long-time series remote sensing images
Firozjaei, M. K. et al. Seyed Kazem Alavipanah, A historical and future impact…
#Environment #Coaldevelopment #ecology #Environmentalsciences #Environmentalsocialsciences #Googleearthengine #HumanitiesandSocialSciences #multidisciplinary #NormalizedDifferenceVegetationIndex #science #Shendongminingarea #Surfacevegetation
https://www.europesays.com/3211867/
Research on the spatiotemporal evolution characteristics of NDVI in Shendong mining area based on GEE long-time series remote sensing images https://www.byteseu.com/2302236/ #CoalDevelopment #ecology #environment #EnvironmentalSciences #EnvironmentalSocialSciences #GoogleEarthEngine #HumanitiesAndSocialSciences #multidisciplinary #NormalizedDifferenceVegetationIndex #Science #ShendongMiningArea #SurfaceVegetation
https://www.europesays.com/3211867/ Research on the spatiotemporal evolution characteristics of NDVI in Shendong mining area based on GEE long-time series remote sensing images #CoalDevelopment #ecology #Environment #EnvironmentalSciences #EnvironmentalSocialSciences #GoogleEarthEngine #HumanitiesAndSocialSciences #multidisciplinary #NormalizedDifferenceVegetationIndex #science #ShendongMiningArea #SurfaceVegetation
Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
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https://doi.org/10.3390/rs18142282 <-- shared paper
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https://www.usgs.gov/publications/comparing-desis-hyperspectral-and-landsat-10-simulated-superspectral-data-crop-type <-- shared USGs publication page
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H/T @USGS
“How can we get better at classifying crops from space? 🛰️🌽
Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
Here's what the researchers found:
• Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
• Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
• Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
• The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
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“HIGHLIGHTS:
• What are the main findings?
- The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
- When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
• What are the implications of the main findings?
- A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
- This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
#hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
@USGS
Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
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https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
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https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
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H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
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“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal
Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
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https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
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H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability
Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).
💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/
#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
Hello Mastodon, an #introduction.
I'm a geospatial scientist with a PhD in Earth System and Geoinformation Science. I work in remote sensing and #GeoAI, mostly on drought, vegetation, and land — in Africa and the US Great Plains.
I'll mostly post maps, model results, and #GoogleEarthEngine and Python tutorials (#geopandas, #rasterio). I'm also building a national GIS data portal for #Cameroon.
#RemoteSensing #GIS #EarthObservation #Python
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
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https://doi.org/10.3390/w18070844 <-- shared paper
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https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
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#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence
S2Coast-2023 - The First Global 10-Meter Resolution #Coastline Dataset Derived From Enhanced #Sentinel2 Composite #Imagery Using #GoogleEarthEngine
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https://doi.org/10.1016/j.rse.2025.115186 <-- shared paper
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https://zenodo.org/records/17092775 <-- shared data
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“HIGHLIGHTS:
• Developed a GEE-based global coastline detection framework (S2Coast).
• HWLSentinel-2 serves as a unified and stable indicator for global coastline detection.
• The S2Coast-2023 coastline dataset is generated from annual Sentinel-2 imagery.
• Pixel-level accuracy compared with VHR-based OpenStreetMap coastlines..."
#GIS #spatial #mapping #RemoteSensing #CoastalResearch #ClimateChange #EarthObservation #Globalcoastlinemapping #Senitinel #googleearthengine #GEE #Compositing #coast #globe #global #model #modeling #detection #marine #terrestrial #ecosystems #knowledgebased #framework #spatialanalysis #spatiotemporal #QAQC
Google rolls out WeatherNext 2 for sharper forecasts
https://web.brid.gy/r/https://nerds.xyz/2025/11/google-weathernext-2-ai-model/
Google ha publicado un nuevo dataset en su plataforma Google Earth Engine y está dando financiación para miniproyectos que lo usen. Me han propuesta que intente conseguir uno de estos miniproyectos --porque suma como IP que es tremendamente complejo de conseguir en España para noveles como yo (o eso dicen). El problema es que, según tengo entendido, Google está dando soporte al genocidio en gaza perpetrado por Israel.
Como me da urticaria saber esto, estoy sopesando si pedirlo o no. En caso de hacerlo, seguramente le proponga al jefe de hacerlo sobre la extensión del genocidio en Gaza. Seguramente no pasaría el filtro, pero no veo otra forma de usar el dinero de Google si no es para denunciar a Israel.
Por otro lado, la UE no se queda atrás en cuánto al genocidio y es la que financia uno de los proyectos en los que he estado trabajando.
Atrapado me siento.
#Google #GoogleEarthEngine #GenocidioEnGaza #Israel #Gaza #UE #UniónEuropea #Genocidio #Palestina
„Wirtualny satelita” od Google. AlphaEarth Foundations widzi więcej i dokładniej niż kiedykolwiek
Google DeepMind oraz Google Earth Engine zaprezentowały AlphaEarth Foundations – nowy, fundamentalny model sztucznej inteligencji, który ma zrewolucjonizować sposób, w jaki monitorujemy i analizujemy zmiany na powierzchni Ziemi.
Narzędzie działa jak „wirtualny satelita”, łącząc dane z wielu źródeł, aby stworzyć spójny i niezwykle szczegółowy obraz naszej planety.
Nowy model AI rozwiązuje dwa kluczowe problemy w obserwacji Ziemi: przeciążenie danymi oraz niespójność informacji pochodzących z różnych satelitów. AlphaEarth Foundations integruje petabajty danych – od optycznych zdjęć satelitarnych, przez radar, po symulacje klimatyczne – w jedną, zunifikowaną cyfrową reprezentację. Analizuje całą powierzchnię lądową i wody przybrzeżne Ziemi z precyzją do kwadratów o boku 10 metrów.
Kluczową innowacją jest tworzenie bardzo kompaktowych podsumowań danych, które wymagają 16 razy mniej miejsca na dysku niż w przypadku innych testowanych systemów AI. Jak podaje Google, w testach porównawczych model AlphaEarth Foundations był stale najdokładniejszy, osiągając średnio o 24% niższy wskaźnik błędu niż inne testowane modele.
Our new AI model AlphaEarth Foundations is mapping the planet in astonishing detail. 🌏🔍
Scientists will now be able to track the impact of deforestation, monitoring crop health, and more – significantly faster, thanks to our new datasets. 🧵 pic.twitter.com/rbojUifykg
— Google DeepMind (@GoogleDeepMind) July 30, 2025
Dane dostępne dla wszystkich
Aby przyspieszyć badania i umożliwić nowe zastosowania, Google udostępnia roczne zbiory danych wygenerowane przez model w ramach publicznego zestawu „Satellite Embedding dataset” w Google Earth Engine. Dzięki temu naukowcy, rządy i organizacje pozarządowe mogą prowadzić zaawansowane analizy, często przy użyciu zaledwie kilku linijek kodu.
Z danych korzysta już ponad 50 organizacji na całym świecie, w tym Organizacja Narodów Zjednoczonych do spraw Wyżywienia i Rolnictwa (FAO), Group on Earth Observations (GEO) oraz czołowe uniwersytety, jak Stanford czy Oregon State University.
„Zestaw danych Satellite Embedding rewolucjonizuje naszą pracę, pomagając krajom w mapowaniu niezbadanych dotąd ekosystemów – jest to kluczowe dla ustalenia priorytetów w działaniach na rzecz ochrony przyrody” – mówi Nick Murray, dyrektor Global Ecology Lab na James Cook University i lider projektu Global Ecosystems Atlas.
Innym przykładem jest organizacja MapBiomas, która wykorzystuje dane AlphaEarth do mapowania brazylijskich ekosystemów i analizowania zmian w rolnictwie. „Ten zestaw danych może odmienić sposób, w jaki pracuje nasz zespół – mamy teraz nowe możliwości tworzenia map, które są dokładniejsze, precyzyjniejsze i szybsze w produkcji” – powiedział Tasso Azevedo, założyciel MapBiomas. Google opublikowało również wideo (załączone powyżej z postu na X), na którym można zobaczyć, jak model radzi sobie z „widzeniem” przez chmury w Ekwadorze czy mapowaniem złożonej powierzchni Antarktydy.
#AI #AlphaEarthFoundations #Google #GoogleDeepMind #GoogleEarthEngine #MapBiomas #mapowanie #news #obserwacjaZiemi #sztucznaInteligencja #zmianyKlimatu
#GEE #GoogleEarthEngine #Academia #Research
I use GEE for research and academic purposes (non-commercial) and I received an email from Google Cloud to verify my non-commercial eligibility. Is there anyone out there in the #Fediverse that can provide recommendations on filling out their questionnaire to ensure I don’t get kicked off and forced to pay? Please #Boost muchas gracias 🙏
High Spatial-Resolution Satellite Mapping Of Suspended Particulate Matter In Global Coastal Waters Using Particle Composition-Adaptive Algorithms
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https://doi.org/10.1016/j.rse.2025.114745 <-- shared paper
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https://tssmapping.projects.earthengine.app/view/sscmap <-- shared SedXplorer: Global Coastal Suspended Sediment Explorer (web mapping tool)
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#GIS #spatial #mapping #remotesensing #coastal #coast #sediment #dynamics #suspendedparticulatematter #particlecomposition #watertype #classification #Landsat #GoogleEarth #GoogleEarthEngine #GEE #opendata #spatialanalysis #spatiotemporal #sedimentology #SPM #earthobservation #model #modeling #marine #estaurine #water #hydrology #geomorphology #global #river #erosion #waterquality #turbidity
So it seems from the lack of responses to my previous post https://fediscience.org/@jgomezdans/114427792187001051 that no one is using the #Copernicus #DataSpace via #STAC. I guess most academic #geo peeps use #GoogleEarthEngine or just download the data files directly? Is STAC only really used by commercial #satellite "bros"?
Striking Satellite Images Reveal Antarctica Is 10 Times Greener Than 35 Years Ago
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https://www.sciencealert.com/striking-satellite-images-reveal-antarctica-is-10-times-greener-than-35-years-ago <-- shared technical article
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https://doi.org/10.1038/s41561-024-01564-5 <-- shared paper
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#GIS #spatial #mapping #remotesensing #landsat #change #Antarctic #AntarcticPeninsula #spatialanalysis #spatiotemporal #climatechange #greening #vegetation #change #warming #satellite #browning #cryosphere #anthropogenic #warming #moss #terrestrial #ecosystems #GoogleEarthEngine #NDVI #TCG #icefree #model #modeling
New [2018] 30 Metre Resolution Global Shoreline Vector And Associated Global Islands Database For The Development Of Standardized Ecological Coastal Units
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https://doi.org/10.1080/1755876X.2018.1529714 <-- shared 2018 paper
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#GIS #spatial #mapping #coast #coastline #global #shoreline #landsat #remotesensing #spatialanalysis #extraction #AI #machinelearning #GSV #GSHSS #ecology #terrestrial #marine #ECU #waters #GEO #GoogleEarthEngine #MBON #BluePlanet #water #hydrography #hydrospatial #GroupOnEarthObservations
@GroupOnEarthObservations #islands @GEOSEC2025
Cropland Abandonment Between 1986 And 2018 Across The United States - Spatiotemporal Patterns And Current Land Uses
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https://doi.org/10.1088/1748-9326/ad2d12 <-- shared paper
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https://www.agdaily.com/crops/study-30-million-acres-cropland-abandoned-since-1980s <-- shared media article
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#GIS #spatial #mapping #spatiotemporal #USA #spatialanalysis #crops #cropland #abandoned #remotesensing #landsat #imagery #CONUS #AI #USDA #OgallalaAquifer #GoogleEarthEngine #CNN #machinelearning #irrigation #ConservationReserveProgram #landuse #landcover #planning #management #economics
#30DayMapChallenge Day 23: Memory
This map by Noel Gorelick, co-founder of #GoogleEarthEngine & newest Kontur team member, combines Hansen Global Tree Cover/Loss dataset & 80+ workshop locations (from memory) that helped Earth Engine "map" its impact 🌲🌍.
#30DayMapChallenge2024
This has been a long time coming, and not the end of it either. The slow squeeze of Google Earth Engine has started.
First came the commercial offerings, now they are pushing people by default to Google Cloud (i.e. have your credit card on the ready).
For those on the free tier, you will see less capacity (stronger quota). And, you will be squeezed, again and again. The market uptake is high enough, so the free ride is over.
This composite is created by my #GoogleEarthEngine app, which provides a very simple interface for creating Sentinel-1-based multitemporal composites. The open-access app can be used by anyone interested in exploring this technique's capabilities.
Find the app here 👉 https://niklasjordan.users.earthengine.app/view/multitempviewer
[🧵 3/3]
@sonaguliyeva et al. (2023) applied #machinelearning algorithms in mapping #croptypes using high resolution #Azersky imagery & demonstrated the value of #opensource platforms like #GoogleEarthEngine in the #classification of crops #LoLManuscriptMonday https://bit.ly/Guliyeva_2023
Shout out to Sona's co-authors & affiliated organizations #Azercosmos #NationalAviationAcademy #Azerbaijan #EOChat #GISChat
Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka [India]
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https://doi.org/10.58825/jog.2023.17.1.75 <-- shared paper
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#GIS #spatial #mapping #spatialanalysis #Mango #Coconut #Areaestimation #Machinelearning #ConvolutionalNeuralNetwork #CNN #RandomForest #RF #agricultural #agriculture #farming #estimates #Karnataka #India #acreage #production #horticulture #commodities #export #remotesensing #satellite #GoogleEarthEngine #GEE #Copernicus #Sentinel2 #algorithms #learning #google #ml
Our newest episode of #SceneFromAbove with special guest @sabrinaszeto has launched TODAY! 🚀✨
Tune in to hear about Sabrina's work with #GoogleEarthEngine & #EarthEngineUserMeetup, advocacy through Women+ in Geospatial, & more! 🎉🛰️ #EOChat #gischat
⭐ S13E5 link ⭐ :
https://scenefromabove.podbean.com/e/s13e5-empowering-geospatial-innovation-with-sabrina-szeto/
Thanks to Sabrina for joining us! And thanks to @rafaelatiengo, @morganahcrowley & @flaviamendes for your efforts this episode! 🛰️
✨Season 13 of #SceneFromAbove is brought to you by Geoawesomeness and UP42 ✨
.@IleanaCallejas et al. (2021) analyze imagery from #MODIS #Aqua from #GoogleEarthEngine with marine traffic & precipitation data to establish an improvement in water quality in Belize Coastal Lagoon during the #COVID19 anthropause. #LoLManuscriptMonday https://bit.ly/Callejas_2021
Hey #GoogleEarthEngine enthusiasts! #eemont v0.3.6 has been released! 🚀😉
Check it here: https://github.com/davemlz/eemont
🆕 #AwesomeSpectralIndices v0.3.0 🛰️🌿
🆕 #Radar indices computation support for #Sentinel1 and #Palsar2!
I recently made a useful addition to my #GEE code which allows me to see when it was last run in local time while ingesting data in the community catalog
You can try it yourself. I am channeling @Keiko_geo #EarthEngine tips series 😀
https://code.earthengine.google.com/79a474f67640936683b47f1989cf3ece
I just found out there is people creating YouTube video tutorials about #AwesomeSpectralIndices and #GoogleEarthEngine ❤️
Here is one about #spectral by @ksabmagar7. Keep up the nice work!
Link: https://www.youtube.com/watch?v=GJuoBp6B3fU&ab_channel=ktmagar%27sYT
RT @YellowstoneNPS: To analyze the current extent of aspen in northern Yellowstone, the @NASA_DEVELOP team refined ecological models in #GoogleEarthEngine using @NASA_Landsat and @CopernicusEU imagery to classify aspen stands. #DEVELOPinthePark
🐦🔗: https://n.respublicae.eu/CopernicusEU/status/1601542280036642816
Hey #GoogleEarthEngine enthusiasts! #spectral 0.3.0 has been released! Now you have access to the new version of #AwesomeSpectralIndices in the Code Editor 😉 ⬇️
🏅 228 Spectral Indices! 🛰️ 🌿
Animated gif with leafmap #geospatial #GoogleEarthEngine