Remote Sensing Applications

AI can now show where the water may go before the river rises. 🌊🗺️ Google’s Flood Hub is a free, public platform that combines AI, hydrological modelling, weather information and geospatial data to provide: 🌊 River water trends ⚠️ Real-time flood forecasts 🗺️ Local inundation maps 🔔 Flood alerts For riverine flooding, forecasts may be available up to seven days in advance, helping communities, governments and aid organisations prepare earlier. The information is updated regularly and presented on an easy-to-use interactive map. For agriculture, this kind of technology could support better decisions around: Identifying farms and villages likely to be affected Prioritising farmer alerts and field visits Protecting livestock, machinery and stored produce Planning evacuation and relief routes Estimating potential crop and infrastructure exposure The most exciting part is not just the AI model. It is the ability to convert complex flood forecasts into information that people can actually see, understand and act upon. This is what practical geospatial AI should look like: Predict early. Map clearly. Communicate locally. Act quickly. Explore Google Flood Hub: https://lnkd.in/gQQ73UgV Flood Hub information is approximate and should be used alongside warnings and instructions from official local authorities. #GoogleFloodHub #GeospatialAI #FloodForecasting #ClimateTech #Agritech #GIS #RemoteSensing #DisasterManagement #ClimateResilience #ArtificialIntelligence

I'm thrilled to share three open-source github projects developed by our team at Google DeepMind, in collaboration with Google Research, aiming at advancing geospatial AI research and understanding our 🌍: 1️⃣ Jeo (Jax for Earth Observation): Geospatial model training for Earth Observation with Jax / Flax. 🔗 https://lnkd.in/dG-w4YYv 2️⃣ GeeFlow: Large-scale geospatial datasets generation and processing with Google Earth Engine (GEE). 🔗 https://lnkd.in/dd6TxnNz 3️⃣ ForesTypology: Datasets to protect Earth's forests and biodiversity. Includes global forest layers (created with partners) and benchmark datasets to motivate AI research in forest types mapping. 🔗 https://lnkd.in/dSF2hBQQ We believe in the power of open science and collaboration to tackle some of the world's most pressing environmental challenges, and share these initial frameworks early. To be updated... Stay tuned! #GoogleDeepMind #GeospatialAI #EarthObservation #OpenSource #GIS #RemoteSensing #GoogleEarthEngine #ForesTypology #AIforGood #EnvironmentalScience

NASA and IBM's #Prithvi just became the first geospatial #foundationmodel ever deployed in orbit. It's now running on the #Kanyini satellite and on a payload aboard the International Space Station. Satellites collect huge amounts of data, but bandwidth back to Earth is limited, so the #AI models running onboard today are usually small and locked to a single task. with a #foundation model it works differently, you upload one big general model, and when you want to teach the #satellite something new, you only send up a small decoder. the team tested this with flood and cloud detection, processing the imagery directly in orbit before it ever came home. #Prithvi was trained on 13 years of #Landsat and #Sentinel2 data, and it can be adapted for tasks like flood mapping, fire monitoring, and crop yield prediction. And because it's #opensource, anyone can build on it. This is the direction I'm most excited about in #remotesensing right now, not just bigger models, but moving the intelligence closer to where the data is actually being captured. Link to the Prithvi model: https://lnkd.in/g9K8RNKn

Sentinel-2 Deep Resolution 3.0 (S2DR3) marks a major leap forward in enhancing Earth observation imagery achieving effective 12-band, 10× single-image super-resolution across Sentinel-2 L2A data. Optimized to preserve subtle spectral variations in soil and vegetation, the model reconstructs spatial features down to 3 m with remarkable spectral fidelity. This enables next-generation insights for precision agriculture, environmental monitoring, and carbon verification (MRV) applications. What stands out most is not just the architecture, but the balance between spectral integrity and spatial precision, achieved through meticulous attention to model design and optimization. It should be said, however, that in my experience four key factors have the greatest impact on the attainable performance of such models, in the following order of decreasing importance: 1️⃣ Training data 2️⃣ Loss function 3️⃣ Hyperparameters 4️⃣ Network architecture 🛰️ S2DR3 Google Colab notebook is available for testing and validation. A remarkable step forward in how AI continues to reshape remote sensing moving from visualization to truly analytical applications. #RemoteSensing #EarthObservation #AIinGeospatial #Sentinel2 #DeepLearning #GeospatialAI #PrecisionAgriculture #EnvironmentalMonitoring #MRV #DataScience #MachineLearning #SatelliteImagery #SuperResolution #ClimateTech #Geoinformatics #SpatialAnalytics #OpenData #AIResearch #SustainableInnovation #GIS

I spent 20 hours analyzing 5 breakthrough Earth Disasters AI Agents from Stanford, MIT, and NASA's Jet Propulsion Lab. Here's the life-saving architecture that's changing disaster response forever ⬇️ Most AI systems clean up after disasters. 》𝗧𝗵𝗲 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗚𝗲𝗼-𝗔𝗴𝗲𝗻𝘁𝘀 These research teams built geo-agents that triangulate risk by combining three weak signals most systems ignore. Individually these signals mean nothing. Combined, they predicted the 2023 Turkey earthquake 72 hours early in simulations. 》𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗕𝘂𝗶𝗹𝘁 𝗧𝗵𝗶𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ✸ Data Sources & Agent System: ☆ Seismic Agent: Monitors ground movement from LSTM + Transformer models ☆ Satellite Agent: Processes visual changes using computer vision ☆ Weather Agent: Tracks rainfall & temperature via APIs ☆ Historical Pattern Agent: Analyzes past disaster data ☆ Prediction Agent: Combines conflicting signals for ensemble prediction ✸ The Key Insight: ☆ When satellite shows dry land BUT weather predicts heavy rain AND historical data flags flood season = 72-hour warning ☆ Weak signal detection through contradiction analysis ☆ Multi-agent orchestration beats single-model approaches ✸ Tech Stack: ☆ Reasoning LLMs for causal analysis ☆ Groq for real-time processing ☆ LangGraph for agent orchestration ☆ ChromaDB for geospatial embeddings 》𝟱 𝗚𝗲𝗼 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗣𝗮𝗽𝗲𝗿𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 ✸ 1. GeoChat: Grounded Large Vision-Language Model for Remote Sensing ☆ Key Feature: Conversational querying for geospatial data ☆ Benefit: Non-experts extract insights with natural language prompts ✸ 2. GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks ☆ Key Feature: Standardized benchmarking for geospatial VLMs ☆ Benefit: Robust model evaluation with consistent metrics ✸ 3. RS5M: A Large-Scale Vision-Language Dataset for Remote Sensing ☆ Key Feature: Massive dataset of image-text pairs ☆ Benefit: Fine-tunes models for disaster monitoring tasks ✸ 4. VHM: Versatile and Honest Vision Language Model for Remote Sensing ☆ Key Feature: High interpretability for sensitive applications ☆ Benefit: Builds trust in AI for disaster response and policymaking ✸ 5. EarthGPT: Universal Multi-modal LLM for Multi-sensor Image Comprehension ☆ Key Feature: Multimodal analysis combining multisensor data ☆ Benefit: Integrates diverse datasets for richer insights ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 trusted by 1,500+ worldwide! ➠ Build Geo, Audio, Video & Vision Agents ➠ Master 5 Modules: 𝗠𝗖𝗣 · LangGraph · PydanticAI · CrewAI · OpenAI Swarm ➠ Deploy for Healthcare, Finance, Smart Cities & More 👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙): https://lnkd.in/eGuWr4CH

𝗣𝗶𝘅𝗲𝗹-𝗟𝗲𝘃𝗲𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆 While large multimodal models excel at understanding natural images, they struggle with satellite and aerial imagery. The unique overhead perspective, scale variation, and small objects in high-resolution remote sensing data present distinct challenges that current models can't handle effectively. Akashah Shabbir et al. introduced GeoPixel, the first large multimodal model designed specifically for high-resolution remote sensing image analysis with precise pixel-level grounding capabilities - meaning it can identify exactly which pixels in an image correspond to objects it discusses. 𝗪𝗵𝘆 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗻𝘀𝗶𝗻𝗴 𝗶𝘀 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: Remote sensing imagery requires specialized understanding that general vision-language models lack: • Overhead viewpoints create spatial relationships unlike natural photography • Extreme scale variations - from individual vehicles to entire city blocks in one image • Small objects distributed across vast areas require precise localization • Limited training data with conversations where text references are linked to specific image regions 𝗧𝗵𝗲 𝗚𝗲𝗼𝗣𝗶𝘅𝗲𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: The system combines three key components to handle high-resolution imagery up to 4K: • 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗶𝗺𝗮𝗴𝗲 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Divides images into local and global regions for efficient processing • 𝗣𝗶𝘅𝗲𝗹-𝗹𝗲𝘃𝗲𝗹 𝗴𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴: Generates precise segmentation masks that show exactly which pixels belong to each object mentioned • 𝗜𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗲𝗱 𝗺𝗮𝘀𝗸 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Produces detailed responses with corresponding visual masks in conversation 𝗧𝗵𝗲 𝗚𝗲𝗼𝗣𝗶𝘅𝗲𝗹𝗗 𝗗𝗮𝘁𝗮𝘀𝗲𝘁: To enable grounded conversations, the researchers created a specialized dataset using a multi-tier annotation strategy: • 54k grounded phrases linked to 600k objects • Descriptions averaging 740 characters with rich spatial context • Hierarchical annotations from scene-level context to individual object details • 5k validated referring expression-mask pairs for evaluation 𝗞𝗲𝘆 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: This capability enables more precise analysis for: • Urban planning and infrastructure mapping • Environmental monitoring and change detection • Disaster response and damage assessment • Agricultural monitoring and precision farming • Defense and security applications 𝗜𝗺𝗽𝗮𝗰𝘁: GeoPixel addresses a critical gap in AI applications for EO. By enabling natural language conversations about satellite imagery with pixel-accurate grounding, it could accelerate decision-making in fields from urban planning to climate research. The model and dataset are publicly available to advance the field. https://lnkd.in/ejJs6aFc #RemoteSensing #ComputerVision #MachineLearning #SatelliteImagery #EarthObservation

AI image processing aboard satellites in space has been a goal of the Earth observation industry for years. Now it has finally been achieved. Planet Labs, based in Calif., released an image captured by its Pelican-4 multispectral satellite showing an airport in Alice Springs, Australia. On the tarmac, more than a dozen aircraft are scattered, each highlighted in a neat green box, identified by an AI model running aboard the satellite. Planet Labs’ engineers had worked 18 months to accomplish reliable autonomous object classification from space. They hope the technology will put Earth observation on steroids, enabling autonomous tasking and real-time sharing of insights with users on Earth.

AI-powered satellites are quietly changing how we monitor the planet. The problem 👇 • Critical sites (construction, borders, pipelines, farms) are monitored late, manually, or not at all • Ground inspections are slow, expensive, and often reactive • By the time issues are spotted, damage is already done The agitation 😬 Missed land encroachments. Undetected structural changes. Environmental violations discovered too late. Decisions made with outdated or incomplete data. This isn’t a data problem. It’s a visibility problem. The solution ;: AI-powered satellites now monitor sites in near-real time. They detect changes, patterns, and risks automatically — without boots on the ground. ✔️ Track site progress ✔️ Detect anomalies early ✔️ Monitor large areas continuously ✔️ Make faster, data-backed decisions From infrastructure and agriculture to energy and urban planning — satellite + AI = proactive monitoring, not reactive damage control. The future of site monitoring isn’t more people. It’s better eyes in the sky. 💬 What use case do you see AI satellites impacting the most? #AI #SatelliteTechnology #GeospatialAI #RemoteSensing #SiteMonitoring #Infrastructure #ClimateTech #SmartCities #DataDriven #Automation

Artificial Intelligence in Earth Science - A GeoAI Perspective -- https://lnkd.in/gT594PG3 <-- shared paper -- “ABSTRACT: GeoAI, or geospatial artificial intelligence (AI), has transformative potential for Earth science by integrating geospatial data with AI to enhance environmental monitoring, predictive modeling, and decision-making. This commentary, based on the Greg Leptoukh Lecture at American Geophysical Union 2024, explores the evolving role of GeoAI in addressing pressing challenges—from environmental change in the Arctic to disaster response in hurricane-prone tropical regions. It highlights advancements in GeoAI-driven analysis of multimodal Earth observation data, ranging from structured remote sensing imagery to semi-structured data, and natural language texts. The integration of knowledge graphs and generative AI further strengthens GeoAI by enabling seamless integration of cross-domain data, semantic reasoning, and knowledge inference. By bridging informatics and domain expertise, GeoAI is shaping a more intelligent and actionable digital future for Earth science. PLAIN LANGUAGE SUMMARY: In December 2024, [the author] had the opportunity to give the Greg Leptoukh Lecture at the American Geophysical Union (AGU) Fall Meeting in Washington, D.C. In [their] talk, [they] discussed how artificial intelligence (AI), especially its geospatial branch known as GeoAI, is helping scientists process and analyze large, complex Earth observation data more efficiently. This commentary summarizes key points from that lecture, highlighting important research at the intersection of GeoAI and Earth science. It also explores ongoing challenges and future directions for using AI to build a smarter and more sustainable digital future for our planet. KEY POINTS  • GeoAI supports environmental monitoring and decision-making through artificial intelligence (AI) powered analysis of Earth observation data  • GeoAI advances by using multimodal Earth data and generative AI to improve understanding of complex Earth systems  • GeoAI needs scientific validation and reproducibility to support reliable and trustworthy Earth science research…” #GIS #spatial #mapping #GeoAI #earthscience #geology #engineeringgeology #AI #artificialintelligence #risk #hazard #decisionmaking #disasterresponse #hurricane #publichealth #disaster #response #spatialanalysis #model #modeling #bigdata #analysis #productionpipeline #temporal #multidimensional #realtime #forecasting #visualisation #challenges #environmental #remotesensing #earthobservation #usecase #crossdomain #semantic #reasoning #informatics #research #multimodal #generativeAI #earthsystems #reliable #trustworthy

4,255 locations. Generated from drone imagery. Each one with a damage assessment, a cost estimate, and photo evidence. All from aerial photos alone. That is what the remote sensing tool produced for the American Red Cross in Saipan after Typhoon Sinlaku. 150 gigabytes of drone imagery ingested, processed, and turned into structured damage data. No one set foot on the ground for those assessments. Here is how it works. The drone captures imagery with GPS coordinates, altitude, pitch, and yaw baked into the metadata. The tool ingests the image, detects and classifies damage, creates a location using reverse geocoding, and generates a residential assessment with structural damage summary, interior damage assessment, estimated cost, and photo documentation. From a single photo. Every assessment is tagged for ground truth verification. The AI does not make the final call. A human verifies. That is the design principle: technology fades into the background and allows the subject matter expert to focus on the task. For field operations, the platform offers three capture methods: → Manual entry for detailed assessments → Voice AI that interviews the assessor through the damage description → Quick Capture for experienced assessors who can record and generate a complete assessment in under 30 seconds The Red Cross's new disaster assessment director, Abraham Mulberry, flagged situational intelligence as the key to understanding the organization's liability after the Mississippi tornadoes. Not just damage counts. A rapid picture of what the organization is on the hook for, in real time, so leadership can make resource allocation decisions before the first volunteer hits the ground. The cost estimation methodology runs in priority order: your organization's own data first, then affiliated organizations, then public data, then general knowledge. Transparent. Documented. Defensible. That is the scale that remote sensing makes possible. The human still verifies. The AI just makes sure the human is verifying the right things instead of collecting data from scratch.