Land MDPI’s Post

Landslide predictions could become 94 percent more accurate: Researchers improve landslide predictions with machine learning and aerial data, achieving up to 94% accuracy in identifying landslide types. #EarthDotCom #EarthSnap #Earth

Enhance your geological data analysis with ElasticDocs. 🌏 #ElasticDocs allows #engineers and #geoscientists to swiftly navigate thousands of report pages and identify #geological analogs using advanced search-and-cluster techniques. This #innovative solution is crafted by Iraya’s team of expert machine learning specialists and #geoscientists. #UnstructuredData #LargeLanguageModelling #ComputerVision #DataAtelier #earth #energy #environment #readtheearthbetter #IrayaEnergies #DataInsights #DataDrivenExploration #DataDrivenDiscovery #InnovateWithIraya #AIForEarthInsights #EarthInsightsAI

Detecting stochastic behaviour and scaling laws in time series of geomagnetic daily means https://t.co/cGdPug7dbx #EurekaMag

Excited to share our research #article entitled "Regional‑scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)"! Our study proposes a method that uses a #MachineLearning approach for both spatial and temporal #landslide forecasting at the regional scale. The aim is to overcome the traditional static applications of machine learning and demonstrate the applicability of a novel model aimed at spatiotemporal landslide #hazard mapping, with perspectives of applications to early warning systems. Check it out in the Landslide journal! Open Access: https://lnkd.in/dF_KN7kj Thanks to the team: Ascanio Rosi, Samuele Segoni, Luca Piciullo, Zhongqiang Liu, Riccardo Fanti. Machine Intelligence and Slope Stability Laboratory NGI - Norwegian Geotechnical Institute Università degli Studi di Firenze #artificialintelligence #RandomForest #openaccess #slopestability #earlywarning

I' m happy to share with you our latest findings on the computation of a quasi geoid model over Africa

🌍 Exploring the Dynamic World of Point Bars with Remote Sensing and Machine Learning 📊✨ Hello LinkedIn Network! I’m excited to share my latest research on the Geological Temporal Analysis and Geometric Characterization of Point Bars along the rivers, utilizing cutting-edge technologies such as Remote Sensing and Machine Learning. 🔍 Project Highlights: Temporal Analysis: Investigating how point bars evolve over time to better understand sedimentary processes and river dynamics. Geometric Characterization: Analyzing the spatial distribution and morphology of point bars to reveal patterns and changes in river morphology. Remote Sensing: Leveraging high-resolution satellite imagery to capture detailed and accurate data on point bars. Machine Learning: Applying advanced algorithms to classify and analyze spatial features, enhancing the accuracy and efficiency of the characterization process. 📈 Why It Matters: Understanding point bar dynamics is crucial for effective river management, flood risk assessment, and environmental monitoring. My work provides valuable insights into sediment transport and riverine processes, contributing to better-informed decisions for sustainable river management. #RemoteSensing #MachineLearning #Geology #RiverDynamics #Sedimentology #EnvironmentalMonitoring #GeospatialAnalysis #DataScience Dr. Francis Omonefe (Ph.D)

𝗢𝗿𝗱𝗻𝗮𝗻𝗰𝗲 𝗦𝘂𝗿𝘃𝗲𝘆 𝗲𝘅𝗽𝗮𝗻𝗱𝘀 𝗶𝗻𝘁𝗼 𝗻𝗲𝘄 𝘁𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝗕𝗿𝗶𝘁𝗶𝘀𝗵 𝗚𝗲𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗦𝘂𝗿𝘃𝗲𝘆 Ordnance Survey (OS) is expanding into new territory as a distributor of third-party government data products from the OS Data Hub. OS and the British Geological Survey (BGS) have teamed up to add a range of geological data products to the popular OS Data Hub, making it easier for users to access and use both above and below ground geospatial data. With OS as Great Britain’s leading geospatial data provider and BGS as the leading provider of the UK’s scientific earth data – both members of the Geo6 - this is an exciting new development and paves the way for further collaboration with other third parties. It’s the first time that products from another authoritative government data partner have been made available through the OS Data Hub and OS hopes it will be followed by more, to enable developers, data scientists and geospatial specialists across the nation with easier access to a broader range of geospatial data to support their activities. This new development has been funded by the Government Office of Technology Transfer’s Knowledge Asset Grant Fund which supports public sector organisations to explore or further develop new and exciting uses for their Knowledge Assets. This followed engagement with OS and BGS’ customers that showed potential benefits to both public and private sectors from greater access to geological data. This development will enable customers to access OS and BGS data from a single platform, making it easier than ever to create value from geospatial data. BGS is a data-rich organisation which manages over 400 datasets as well as digital databases and physical collections e.g. borehole core, rocks, minerals and fossils. Nine of their primary datasets will now be included as Open Data in the OS Data Hub. The datasets will provide access to large-scale overview maps of ground hazards, where, for example, there could be landslides that can inform further assessment, and highlight potential hazards that either need to be regraded or investigated in more detail.

This article is about geomagnetic jerks. Geomagnetic jerks

Visualization Tool Helps Oceanographers Predict Sediment Sample Hotspots

🌊 Our recent paper on implementing the #Great_Lakes #Wave #Unstructured (#GLWUv2.0) forecast system at NOAA is now published in Geoscientific Model Development (#GMD)! 📝 During my time at #NOAA (just before I left), we devoted ourselves to bringing the state-of-the-art in #WAVEWATCH_III wave model, which we spent nearly 7 years enhancing for coastal capabilities and modernization, into operational use. The aim of this wave forecast system is to ensure the safety and efficiency of maritime activities in the Great Lakes region. Our paper delves into three key aspects of our work: 1- Operational Guidelines: We developed a robust framework for operationalizing models in highly restrictive environments, such as #NCO/#NCEP/#NWS, ensuring reliability even in the most critical forecasting situations. This framework encompasses aspects such as model reproducibility, simulation time, and coding standards. The outcome was a system that the operational center could operate without deep knowledge about wave models. 2- Scientific Advancements: Through rigorous scientific research, we enhanced the predictability and performance of the GLWUv2.0 model, overcoming numerous challenges posed by operational constraints. We demonstrated that the model is now capable of achieving a few-meter resolution in coastal areas without a penalty in runtime, thanks to the modernization efforts we implemented in WAVEWATCH III. 3- Future Directions: Looking ahead, we discuss the next steps for wave modeling, emphasizing how to improve wave modes across different scales, from global to coastal scales, and the need for wave observations during ice seasons. We also highlight the significance of #coupling our model with other Earth system components like #circulation, #ice, and #hydrology for better forecasting. Our validation against in situ data, covering both summer and wintertime conditions, demonstrates the reliability of GLWUv2.0 in diverse scenarios, including ice season challenges. This article not only sheds light on our achievements but also underscores the ongoing commitment to advancing wave forecasting technology for safer maritime operations in enclosed basins like the Great Lakes. My heartfelt gratitude goes out to my colleagues and friends, Saeideh Banihashemi, Jose-Henrique Alves, Aron Roland, Tyler Hesser, Jane McKee Smith and @Mary Anderson Bryant, whose dedication and expertise were invaluable throughout this endeavor. The GLWUv2.0 is in operation since May 9th, 2023. #WaveModeling #GreatLakes #NOAA #ERDC #CHL #Science #Research #Forecasting #MaritimeSafety 🚢💡