techlist.io
KR EN

remote-sensing

4 posts

google

Separating natural forests from other tree cover with AI for deforestation-free supply chains (opens in new tab)

Researchers from Google DeepMind and Google Research have developed "Natural Forests of the World 2020," an AI-powered global map that distinguishes natural ecosystems from commercial tree plantations. By utilizing high-resolution satellite data and machine learning, the project provides a critical 10-meter resolution baseline to support deforestation-free supply chain regulations like the EUDR. This tool enables governments and companies to monitor biodiversity-rich areas with unprecedented accuracy, ensuring that natural forests are protected from industrial degradation. **The Limitation of Traditional Tree Cover Maps** * Existing maps frequently conflate all woody vegetation into a generic "tree cover" category, leading to "apples-to-oranges" comparisons between different land types. * This lack of distinction makes it difficult to differentiate between the harvesting of short-term plantations and the permanent loss of ancient, biodiversity-rich natural forests. * Precise mapping is now a legal necessity due to regulations like the European Union Regulation on Deforestation-free Products (EUDR), which bans products from land deforested or degraded after December 31, 2020. **The MTSViT Modeling Approach** * To accurately identify forest types, researchers developed the Multi-modal Temporal-Spatial Vision Transformer (MTSViT). * Rather than relying on a single snapshot, the AI "observes" 1280 x 1280 meter patches over the course of a year to identify seasonal, spectral, and textural signatures. * The model integrates multi-modal data, including Sentinel-2 satellite imagery, topographical information (such as elevation and slope), and specific geographical coordinates. * This temporal-spatial analysis allows the AI to recognize the complex patterns of natural forests that distinguish them from the uniform, fast-growing structures of commercial plantations. **Dataset Scale and Global Validation** * The model was trained on a massive dataset comprising over 1.2 million global patches at 10-meter resolution. * The final map provides seamless global coverage, achieving a best-in-class validation accuracy of 92.2% against an independent global dataset. * The research was a collaborative effort involving the World Resources Institute and the International Institute for Applied Systems Analysis to ensure scientific rigor and practical utility. The "Natural Forests of the World 2020" dataset is publicly available via Google Earth Engine and other open repositories. Organizations should leverage this high-resolution baseline to conduct environmental due diligence, support government monitoring, and target conservation efforts in preparation for global climate milestones like COP30.

remote-sensingaimachine-learningcomputer-vision+3
google

Accelerating the magic cycle of research breakthroughs and real-world applications (opens in new tab)

Google Research is accelerating a "magic cycle" where breakthrough scientific discoveries and real-world applications continuously reinforce one another through advanced AI models and open platforms. By leveraging agentic tools and large-scale foundations, the company is transforming complex data into actionable insights across geospatial analysis, genomics, and quantum computing. This iterative process aims to solve critical global challenges while simultaneously uncovering new frontiers for future innovation. ### Earth AI and Geospatial Reasoning * Google has integrated various geospatial models—including those for flood forecasting, wildfire tracking, and air quality—into a unified Earth AI program. * The newly introduced Geospatial Reasoning Agent uses Large Language Models (LLMs) to allow non-experts to ask complex questions and receive plain-language answers derived from diverse datasets. * Riverine flood models have been significantly expanded, now providing forecasts for over 2 billion people across 150 countries. * New Remote Sensing and Population Dynamics Foundations have been released to help researchers understand nuanced correlations in planetary data and supply chain management. ### DeepSomatic and Genomic Research * Building on ten years of genomics work, DeepSomatic is an AI tool designed to identify somatic mutations (genetic variants in tumors) to assist in cancer research. * The tool follows the development of previous foundational models like DeepVariant and DeepConsensus, which helped map human and non-human genomes. * These advancements aim to move the medical field closer to precision medicine by providing health practitioners with higher-resolution data on genetic variations. ### The Magic Cycle of Research and Development * Google highlights "Quantum Echoes" as a key breakthrough in quantum computing, contributing to the broader goal of solving fundamental scientific problems through high-scale computation. * The acceleration of discovery is largely attributed to "agentic tools" that assist scientists in navigating massive datasets and uncovering new research opportunities. * The company emphasizes a collaborative approach, making foundation models available to trusted testers and partners like the WHO and various international research institutes. To maximize the impact of these breakthroughs, organizations should look toward integrating multimodal AI agents that can bridge the gap between specialized scientific data and practical decision-making. By utilizing open platforms and foundation models, the broader scientific community can translate high-level research into scalable solutions for climate resilience, healthcare, and global policy.

remote-sensingaillmfoundation-models+4
google

Google Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning (opens in new tab)

Google Earth AI introduces a framework of geospatial foundation models and reasoning agents designed to solve complex, planetary-scale challenges through cross-modal reasoning. By integrating Gemini-powered orchestrators with specialized imagery, population, and environmental models, the system deconstructs multifaceted queries into actionable multi-step plans. This approach enables a holistic understanding of real-world events, such as disaster response and disease forecasting, by grounding AI insights in diverse, grounded geospatial data. ## Geospatial Reasoning Agents * Utilizes Gemini models as intelligent orchestrators to manage complex queries that require data from multiple domains. * The agent deconstructs a high-level question—such as predicting hurricane landfalls and community vulnerability—into a sequence of smaller, executable tasks. * It executes these plans by autonomously calling specialized foundation models, querying vast datastores, and utilizing geospatial tools to fuse disparate data points into a single, cohesive answer. ## Remote Sensing and Imagery Foundations * Employs vision-language models and open-vocabulary object detection trained on a large corpus of high-resolution overhead imagery paired with text descriptions. * Enables "zero-shot" capabilities, allowing users to find specific objects like "flooded roads" or "building damage" using natural language without needing to retrain the model for specific classes. * Technical evaluations show a 16% average improvement on text-based image search tasks and more than double the baseline accuracy for detecting novel objects in a zero-shot setting. ## Population Dynamics and Mobility * Focuses on the interplay between people and places using globally-consistent embeddings across 17 countries. * Includes monthly updated embeddings that capture shifting human activity patterns, which are essential for time-sensitive forecasting. * Research conducted with the University of Oxford showed that incorporating these population embeddings into a Dengue fever forecasting model in Brazil improved the R² metric from 0.456 to 0.656 for long-range 12-month predictions. ## Environmental and Disaster Forecasting * Integrates established Google research into weather nowcasting, flood forecasting, and wildfire boundary mapping. * Provides the reasoning agent with the data necessary to evaluate environmental risks alongside population density and infrastructure imagery. * Aims to provide Search and Maps users with real-time, accurate alerts regarding natural disasters grounded in planetary-scale environmental data. Developers and enterprises looking to solve high-level geospatial problems can now express interest in accessing these capabilities through Google Earth and Google Cloud. By leveraging these foundation models, organizations can automate the analysis of satellite imagery and human mobility data to better prepare for environmental and social challenges.

remote-sensingaigeminifoundation-models+5
google

Geospatial Reasoning: Unlocking insights with generative AI and multiple foundation models (opens in new tab)

Google Research is introducing Geospatial Reasoning, a new framework that integrates generative AI with specialized foundation models to streamline complex geographical problem-solving. By combining large language models like Gemini with domain-specific data, the initiative seeks to make large-scale spatial analysis accessible to sectors like public health, urban development, and climate resilience. This research effort moves beyond traditional data silos, enabling agentic workflows that can interpret diverse data types—from satellite imagery to population dynamics—through natural language. ### Specialized Foundation Models for Human Activity * The Population Dynamics Foundation Model (PDFM) captures the complex interplay between human behaviors and their local environments. * A dedicated trajectory-based mobility foundation model has been developed to process and analyze movement patterns. * While initially tested in the US, experimental datasets are expanding to include the UK, Australia, Japan, Canada, and Malawi for selected partners. ### Remote Sensing and Vision Architectures * New models utilize advanced architectures including masked autoencoders, SigLIP, MaMMUT, and OWL-ViT, specifically adapted for the remote sensing domain. * Training involves high-resolution satellite and aerial imagery paired with text descriptions and bounding box annotations to enable precise object detection. * The models support zero-shot classification and retrieval, allowing users to locate specific features—such as "residential buildings with solar panels"—using flexible natural language queries. * Internal evaluations show state-of-the-art performance across multiple benchmarks, including image segmentation and post-disaster damage assessment. ### Agentic Workflows and Industry Collaboration * The Geospatial Reasoning framework utilizes LLMs like Gemini to manage complex datasets and orchestrate "agentic" workflows. * These workflows are grounded in geospatial data to ensure that the insights generated are both useful and contextually accurate. * Google is collaborating with inaugural industry partners, including Airbus, Maxar, Planet Labs, and WPP, to test these capabilities in real-world scenarios. Organizations interested in accelerating their geospatial analysis should consider applying for the trusted tester program to explore how these foundation models can be fine-tuned for specific proprietary data and use cases.

remote-sensingaigen-aigemini+5