Remote Sensing Applications

The Future of Forest Protection: LiDAR, Drones & AI 🌲 Forests are one of our most powerful climate solutions, absorbing carbon, regulating ecosystems and supporting biodiversity. But managing them at scale has always been a challenge. For decades, forest monitoring meant teams on foot, manually measuring trees with handheld GNSS receivers, slow, labour-intensive and incomplete. That’s changing fast. NatureTech is revolutionising forest management. Today, LiDAR-equipped drones, AI and real-time data processing allow us to scan 25,000 hectares in hours, with unprecedented precision. 🔍 How it works: ⚬ Drones fly over forests, scanning with LiDAR to capture high-resolution 3D data on trees, terrain and carbon storage. ⚬ AI processes millions of data points, automatically detecting tree species, health status and early signs of decay. ⚬ Real-time monitoring enables us to act before problems escalate, whether it’s illegal logging, pest outbreaks or fire risks. What this means for the future of forests: ✅ Carbon Accounting Becomes 100% Transparent Governments and businesses can now track exactly how much CO₂ forests are absorbing. No more estimates, just verifiable carbon data, making carbon credits and nature investments far more credible. ✅ Smarter, More Resilient Forests AI-powered models will soon predict threats before they happen, allowing teams to prevent pest outbreaks, fire risks and tree diseases, before they spread. ✅ Rewilding at Scale Self-flying drones will restore degraded landscapes, not just mapping forests, but actively dispersing seeds and monitoring regrowth, supercharging rewilding efforts. ✅ The End of Hidden Deforestation Every tree cut, every road built, every illegal clearing will be instantly detected, traced and exposed. Transparency will drive accountability like never before. The biggest shift will be that nature will no longer be seen as a passive resource, but as a living, data-rich infrastructure that we can protect, restore, and invest in with confidence. If we get this right, we could be entering a golden era of forest restoration, where tech finally works for nature, rather than against it. (Source: DJI / SLAM LiDAR / Mistra Digital Forest)

🚢🛰️ Find all the container ships in the Pacific Ocean. Just one of the many wild, once impossible prompts you can now actually run on satellite imagery. Geospatial AI is entering its text-prompt era, and Meta’s new Segment Anything 2 (SAM 2) model is one core piece of the puzzle. But here’s the problem: SAM 2 is powerful but it wasn’t designed to run on terabytes of geospatial imagery. You can’t just point it at massive raster collections and ask it to "segment crop circles" or "find solar farms". 👀 That’s where Wherobots comes in. We just launched support for SAM 2 in Raster Inference, making it possible to run large-scale, text-prompted geospatial segmentation across satellite and aerial imagerymat production scale. You can: ✈️ Use plain language like “find airplanes” or “find basketball courts” 🐍 Run it across massive imagery libraries using SQL or Python 🧊 Get results as Iceberg tables, ready for joins and downstream analysis Behind the scenes, we paired SAM 2 with Google’s OWLv2 to unlock text-to-bounding-box and text-to-segment functionality. This means even if you don’t have deep ML expertise, you can prototype powerful use cases in minutes. This opens the door to faster insight from raw pixels powered by foundational vision models. Check the link in the comments for more details and an example notebook to get started. 🌎 I'm Matt and I talk about modern GIS, geospatial data engineering, and how spatial thinking is changing. 📬 Want more like this? Join 5k+ others learning from my newsletter → forrest.nyc

Today, Nature Communications published our latest research, led by Amit Misra from Microsoft’s AI for Good Lab: a global flood detection model built using 10 years of Synthetic Aperture Radar (SAR) satellite data. It can detect floods through clouds, at night, and in remote areas—filling a critical gap in global disaster data. Already in use in Kenya and Ethiopia, this open-source tool is helping governments respond faster and plan smarter. It’s a powerful example of how AI can drive climate resilience.

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Machine learning is no longer just an analytical tool - it’s becoming the backbone of geospatial intelligence. From wildfire prediction and groundwater mapping to disease forecasting, carbon estimation, and urban sprawl detection, these papers show how spatial data + ML are reshaping environmental risk assessment, urban analytics, agriculture, and climate science. If you're working at the intersection of GIS, remote sensing, and AI - this collection of recent research papers is worth bookmarking. My tutorials: https://lnkd.in/dXpjUz3K ⬇️ Full list in the end ⬇️ 1. Exploration of Geo-Spatial Data and Machine Learning Algorithms for Robust Wildfire Occurrence Prediction https://lnkd.in/dtTW_iau 2. Enhancement of Groundwater Resources Quality Prediction Using an Improved DRASTIC Method and Machine Learning https://lnkd.in/dNhTsieN 3. Remote Sensing-Based Forest Cover Classification Using Machine Learning https://lnkd.in/dZfAUZs4 4. Forest Age Estimation Based on a Machine Learning Pipeline Using Sentinel-2 and Auxiliary Data https://lnkd.in/dr3c79-P 5. Factors of Acute Respiratory Infection Among Under-Five Children Using Machine Learning Approaches https://lnkd.in/d6DUxbAh 6. SAR Image Integration for Multi-Temporal Wetland Dynamics Analysis Using Machine Learning https://lnkd.in/dY4--gep 7. Effects of Non-Landslide Sampling Strategies in Landslide Susceptibility Mapping https://lnkd.in/d7mRkFWv 8. Enhancing Co-Seismic Landslide Susceptibility and Risk Analysis Through Machine Learning https://lnkd.in/dtsigmG8 9. 10-m Scale Chemical Industrial Parks Map Along the Yangtze River Based on Machine Learning https://lnkd.in/dS9qGi68 10. Geospatial Distribution and Machine Learning Algorithms for Assessing Surface Water Quality in Morocco https://lnkd.in/dwxSamAt ... 20. Wheat Crop Genotype Identification Using Multispectral Radiometer Data and Machine Learning 21. Geospatial Data for Peer-to-Peer Communication Among Autonomous Vehicles Using Optimized ML Algorithms ... 𝐅𝐮𝐥𝐥 𝐥𝐢𝐬𝐭: https://lnkd.in/dNC9VA7E

Remote Sensing through Satellite and Drone: An Enabler for Sustainable Agriculture!! As we stand at the intersection of technology and agriculture, one of the most promising advancements is the integration of remote sensing through satellite and drones into our farming practices. This powerful combination is transforming the way we cultivate our lands, ensuring sustainability and productivity go hand in hand. Precision Agriculture 🛰️ Gone are the days of blanket farming, where every inch of the field received the same treatment. With remote sensing, farmers can analyze their fields from above, understanding variations in soil moisture, nutrient levels, and crop health. This enables precise application of resources, reducing waste, and boosting yields. Early Detection of Issues 🌧️ Climate change has brought unpredictable weather patterns and pest infestations. Drones equipped with advanced sensors can monitor crops in real-time. They help in the early detection of issues, allowing farmers to take timely action. This not only minimizes losses but also reduces the need for chemical interventions. Water Management 💧 Water scarcity is a growing concern in agriculture. Satellite imagery aids in assessing the water needs of crops. Drones can then be used for targeted irrigation, reducing water wastage. This is crucial for both sustainable farming and conservation of this precious resource. Crop Health Assessment 👩🌾 Farmers can now easily assess the health of their crops. Satellite and drone imagery can reveal signs of diseases, nutrient deficiencies, or stress. This means timely interventions, leading to healthier crops and less reliance on pesticides. Sustainability and Environment 🌏 Sustainable agriculture is not just about higher yields; it's about conserving the environment. By using remote sensing technologies, we can reduce the environmental footprint of farming. This includes minimizing chemical use, optimizing resource allocation, and mitigating soil erosion. Data-Driven Decision-Making 📊 The data collected through remote sensing is a treasure trove for data-driven decision-making. It empowers farmers with insights and predictions, allowing for proactive strategies and adaptability in an ever-changing agricultural landscape. In conclusion, the marriage of satellite and drone technology with agriculture is a game-changer. It not only enhances productivity but also promotes sustainability and responsible land management. As we face the challenges of feeding a growing global population while preserving our planet, these innovations are a ray of hope. Let's embrace them and work towards a future where agriculture is not just about growing crops but growing a sustainable future for generations to come. I will be addressing multiple sessions at GeoSmart India over the next three days covering this key topic. What are your thoughts on the exciting prospects of remote sensing in agriculture? Let's discuss. #UnclutterFoodAgriculture

Unlocking the Power of GeoAI: From Raw Geospatial Data to Actionable Insights GeoAI is fundamentally changing the way we work with geospatial data. Today, artificial intelligence is not just a research topic, but a practical tool that helps us turn massive amounts of aerial imagery and lidar data into real, actionable information. By combining neural networks with proven photogrammetry and rule-based quality assurance, we can now extract detailed land cover maps, analyze urban surfaces, and even simulate urban climate with a level of precision that was unthinkable just a few years ago. One of the most exciting aspects is how GeoAI enables us to move beyond traditional mapping. With AI-powered segmentation, we can distinguish even the smallest features in urban environments and keep our data up to date. Thanks to TrueOrthos and advanced photogrammetric workflows, geometric distortions are a thing of the past, so data from different times and sensors can be perfectly aligned. This is essential for reliable change detection and multi-source analysis. But the possibilities go even further. Automated analysis of sealed and unsealed surfaces helps cities identify where to prioritize “desealing” for climate resilience. Parcel indexing allows us to aggregate key indicators like green space, building area, or solar installations at any scale, supporting truly data-driven decisions in urban planning and environmental monitoring. And with urban climate simulation, we can combine pixel-precise land cover data with 3D voxel models and CFD to visualize the effects of new trees, green roofs, or lighter pavements, before any construction begins. Even lidar point cloud classification benefits from GeoAI. By combining AI with rule-based checks and external data sources, we achieve robust, scalable, and quality-assured 3D mapping, reducing manual effort and increasing reliability, even in complex or changing environments. GeoAI is already a productive, scalable approach that is shaping the sustainable, data-driven development of our cities and landscapes. With annual updates and hybrid workflows, we ensure that results are not only precise and up to date, but also trusted and actionable. If you want to learn how to turn your geospatial data into valuable information using GeoAI, just reach out or send me a message. Let’s move from data to information, using GeoAI. 💡 Comment | Like | Share   👉 Follow me (Dr. Uwe Bacher) for more Information on exciting topics from the world of geospatial #GeoAI #Geospatial #AerialImagery #Lidar #UrbanPlanning #AI #SmartCities

Mapping wind risk in forests - smarter decisions with LiDAR & AI As extreme storms and cyclones become more frequent, understanding windthrow risk in plantation forests is critical for resilience and sustainability. A recent study with contributions from the New Zealand Institute for Bioeconomy Science Limited leverages multi-temporal LiDAR, optical imagery, and machine learning to predict wind damage in Pinus radiata plantations after Cyclone Gabrielle in New Zealand. Key outcomes: 📌 High model accuracy (AUC = 0.913) using Random Forest 📌 Identified top risk factors: windspeed, exposure, drainage, stand age 📌 Created a wind risk surface to guide forest managers in mitigating losses and planning adaptive afforestation strategies This approach isn’t just for New Zealand - it’s relevant for any cyclone-prone region where plantation forestry matters. Michael Watt I Nicolò Camarretta I Pete Watt I Indufor Ltd #Forestry #ClimateResilience #LiDAR #MachineLearning #SustainableForestry #CycloneGabrielle #ForestManagement #AIinForestry #Science #Technology #Innovation #Bioeconomy https://lnkd.in/gettyuK9

Satellites generate more data in an hour than we can download in a day. Here's why that's about to change. Modern satellites collect an overwhelming amount of information - far more than we can transmit back to Earth quickly. But this isn't just a technical problem. It's potentially costing lives. Here's what's happening right now: When wildfires threaten homes: ↳ Satellite images showing their spread sit trapped for hours During hurricane season: ↳ Vital storm trajectory data reaches emergency teams late - when every minute counts Military operations rely on several-hour-old satellite intelligence ↳ In situations where seconds matter Think about that: We have the data to: • Protect lives • Mitigate disasters • Optimize operations But much of it's stuck in space, waiting to be downloaded. This is why AI-powered satellites are transforming space operations. Take the European Space Agency's new Φsat-2 satellite. Instead of blindly collecting and slowly transmitting back to Earth, it: • Processes images in orbit • Identifies what's actually important • Only sends down actionable intelligence The early indications are game-changing: • 80% reduction in transmission needs • Real-time disaster monitoring • Faster threat detection • Rapid weather pattern analysis Of course, AI in space faces challenges: → Cybersecurity risks → Regulatory constraints → Complex international coordination But the potential rewards are immense for those focusing on: • Reducing data transmission bottlenecks • Providing real-time, actionable insights • Solving critical infrastructure and monitoring challenges This goes beyond a “tech upgrade”. It's a powerful transformation in how we protect communities, save lives, and understand our planet. The old approach: Collect everything, transmit slowly, analyze later. The emerging reality: Think in orbit, send what matters, act immediately. Earth’s early warning systems are getting smarter. P.S: Join high-growth founders and seasoned investors getting deeper analysis on emerging tech trends and opportunities on my newsletter (https://lnkd.in/e6tjqP7y) ____________________________ Hi, I’m Richard Stroupe, a 3x Entrepreneur, and Venture Capital Investor I help early-stage tech founders turn their startups into VC magnets Building in space tech? Let's talk

Keeping the Netherlands dry. Nearly 30% of their country would be underwater. Without advanced water management. Dutch government commitment to digital innovation is simply necessary. Safeguarding millions of people from flooding. One of the most crucial projects is the Actueel Hoogtebestand Nederland (AHN) program led by Jeroen Leusink. A national high-resolution Digital Terrain Model (DTM) and Digital Surface Model (DSM) created using LiDAR technology. This data forms the backbone of flood prevention strategies, helping water authorities to plan and strategize. Water management in the Netherlands isn’t just about holding back the sea—it’s about staying ahead of it. ~ Joris Bak and his team from Esri Nederland built this great application to visualize value of the data. It shows five AHN datasets in very high spatial and temporal resolution. Amazing, explore it here: https://lnkd.in/dH54XTu9 It’s a visual Digital Twin use case at its finest, without makeup and glamour. 🤌 #dtm #dsm #flood #floodmodeling P.S. Do you agree? Have you found value? Your engagement means a lot 🤗

🌍✨ A Complete Guide to Remote Sensing Indices for Geospatial Research Remote sensing has become a powerful tool in understanding our environment, cities, and ecosystems. From monitoring vegetation health to detecting water bodies, identifying urban expansion, or assessing post-disaster impacts—indices play a key role in extracting meaningful insights from satellite imagery. This compilation brings together some of the most widely used indices in GIS & Remote Sensing: 🔹 Vegetation Monitoring: NDVI, EVI, SAVI, GNDVI, VARI, ARVI – to assess greenness, reduce soil effects, and monitor crop/forest health. 🔹 Water & Moisture Detection: NDWI, MNDWI, LSWI – to identify water bodies, wetlands, and soil moisture conditions. 🔹 Urban Studies: NDBI, UI – to detect and differentiate urban built-up areas. 🔹 Soil & Snow Analysis: NDSI, BSI, DPSI – for detecting bare soil, snow, and soil properties. 🔹 Disaster & Stress Monitoring: NBR (burn severity), VHI (vegetation & temperature), CVI (chlorophyll content). 📊 Whether you’re a student, researcher, or practitioner, these indices serve as an essential reference to guide projects in urban planning, agriculture, forestry, disaster management, climate studies, and sustainable development. 🌱 With the right use of spectral indices, we can transform satellite data into actionable knowledge for a more sustainable future. #RemoteSensing #GIS #GeospatialAnalysis #EarthObservation #UrbanPlanning #ClimateAction #SustainableDevelopment #Research