Douha Akkari’s Post

Introducing the geostan R package for spatial analysis. It is designed for areal data and supports various spatial data types. The package includes implementations of the SAR, CAR, and ESF models for spatial regression. #spatialanalysis #Rstas

The Sequential Hierarchical Layer Intersection (SHLI) model in #QGIS offers a structured approach to spatial #DataAnalysis, enhancing the accuracy and efficiency of #GeospatialModelling. This technique involves intersecting layers in a sequential, hierarchical order, where each intersection builds upon the previous one. By applying a predefined hierarchy setup, scientists can progressively refine #SpatialDatasets, focusing on the most relevant intersections at each step. This approach is particularly valuable in complex analyses, such as habitat-selection processes, land-use planning, environmental studies, and multi-layered geographic modelling. It allows for the effective management of large datasets, ensuring that #SpatialRelationships are captured accurately and that the results align with the desired objectives. The ability to control the order of operations in layer intersections makes #QGIS a powerful tool for data scientists, spatial planners, and geospatial professionals, offering clear insights and a streamlined workflow. As the geospatial field continues to evolve, the Sequential Hierarchical Layer Intersection process remains a critical technique for handling multi-layered spatial data with a high level of precision.

🌐 Excited to delve into the world of Stochastic Spatial Interpolation and Processes! 🌟 In today's rapidly evolving landscape of spatial data analysis, understanding stochastic spatial interpolation and processes is paramount. With its innovative techniques, this field offers dynamic insights into environmental, urban, and geospatial phenomena. 🔍 What is Stochastic Spatial Interpolation and Process? Stochastic spatial interpolation involves predicting values at unsampled locations within a geographical area based on available data points. By incorporating randomness and probability distributions, it accounts for uncertainties inherent in spatial data. 🌱 Key Applications: 1️⃣ Environmental Monitoring: Assessing air quality, soil contamination, and species distribution. 2️⃣ Urban Planning: Predicting population density, traffic patterns, and land use changes. 3️⃣ Natural Resource Management: Estimating water availability, forest cover, and crop yield. 🔬 Advanced Techniques: 1️⃣ Kriging: Utilizes spatial correlation to predict values and quantify uncertainty. 2️⃣ Gaussian Processes: Models spatial dependencies using kernel functions, offering flexible predictions. 3️⃣ Monte Carlo Simulation: Generates multiple realizations of spatial patterns, capturing variability. 📈 Benefits: 1️⃣ Enhanced Accuracy: Incorporates spatial autocorrelation and uncertainty estimation. 2️⃣ Improved Decision-Making: Facilitates risk assessment and resource allocation. 3️⃣ Robustness: Adaptable to various data types and spatial scales. 💡 Real-World Impact: From climate modeling to infrastructure planning, stochastic spatial interpolation and processes empower researchers, policymakers, and businesses to make informed decisions in a complex, interconnected world. Let's harness the power of spatial data analytics to unlock new insights and drive positive change! 💪 #SpatialAnalysis #DataScience #GIS #StochasticModeling #SpatialInterpolation #SpatialProcesses #DataAnalytics

There are 3 main challenges of geostatistical modeling: 1) Coregistration of data through change of spatial support 2) Multiple variables 3) Compositional data analysis Learn more about how to overcome these limitations on our blog: https://lnkd.in/gtFwaJm3 #geostatistics #geospatialanalysis

Indices on the fly! Spectral indices or band maths offer a quick way to analyse remotely sensed data on land cover use and types. The band combinations start with multispectral data and extend to hyperspectral. Beyond a snapshot in time, a time series view of an index provides further insights into the changing landscape or a quick change-detection analysis. Eartheye Space delivers this added-on information on the fly, with tasked data and no additional software or hardware to process the data, making the process efficient and the user productive. While we start with NDVI, NDRE from many four bands and above sensors on the platform, over time, we will continue to add more indices to provide information about: 1️⃣ Agriculture or vegetation: a.  𝘊𝘳𝘰𝘱 𝘨𝘦𝘯𝘦𝘳𝘢𝘭 𝘩𝘦𝘢𝘭𝘵𝘩 𝘣.  𝘔𝘰𝘪𝘴𝘵𝘶𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘤.  𝘋𝘪𝘴𝘦𝘢𝘴𝘦/𝘥𝘦𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺 𝘥𝘦𝘵𝘦𝘤𝘵𝘪𝘰𝘯 𝘥.  𝘊𝘳𝘰𝘱 𝘵𝘺𝘱𝘦 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘦.  𝘚𝘰𝘪𝘭 𝘵𝘺𝘱𝘦 𝘢𝘯𝘥 𝘯𝘶𝘵𝘳𝘪𝘦𝘯𝘵𝘴 2️⃣ Urban area: 𝘢.  𝘎𝘦𝘯𝘦𝘳𝘢𝘭 𝘶𝘳𝘣𝘢𝘯 𝘴𝘱𝘳𝘢𝘸𝘭 𝘣.  𝘉𝘶𝘪𝘭𝘵-𝘶𝘱 𝘵𝘺𝘱𝘦 𝘤.  𝘗𝘦𝘳𝘷𝘪𝘰𝘶𝘴-𝘪𝘮𝘱𝘦𝘳𝘷𝘪𝘰𝘶𝘴 𝘢𝘳𝘦𝘢 𝘥𝘦𝘭𝘪𝘯𝘦𝘢𝘵𝘪𝘰𝘯 3️⃣ Mineral: 𝘢.  𝘖𝘳𝘦 𝘵𝘺𝘱𝘦 𝘢𝘯𝘥 𝘴𝘶𝘳𝘧𝘢𝘤𝘦 𝘦𝘹𝘵𝘦𝘯𝘵 𝘣.  𝘐𝘯𝘥𝘪𝘳𝘦𝘤𝘵 𝘮𝘪𝘯𝘦𝘳𝘢𝘭 𝘪𝘯𝘥𝘪𝘤𝘢𝘵𝘰𝘳𝘴 4️⃣ Environment: 𝘢.  𝘞𝘢𝘵𝘦𝘳 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘣.  𝘝𝘦𝘨𝘦𝘵𝘢𝘵𝘪𝘰𝘯 𝘤𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯 𝘤.  𝘋𝘳𝘰𝘶𝘨𝘩𝘵/𝘧𝘭𝘰𝘰𝘥 This is one of many ways Eartheye Space makes earth observation data usable quickly and easily. 🌏 Eartheye Space makes spectral analysis uber-easy. 🛰 Ask for a demo at www.eartheye.space or register at tasking.eartheye.space 💲 Pay as you task, from your annual US$5,000 (min) credit. ✅ Available for any size organisation. ♻ Please repost or share with anyone you know who may be interested in solving strategic and operational issues with Earth Observation. #RemoteSensing #EarthObservation #SatelliteImaging #GeoSpatialData #SpectralIndices #GIS #Tasking #EartheyeSpace 𝘚𝘰𝘶𝘳𝘤𝘦: 𝘎𝘭𝘰𝘣𝘢𝘭 𝘖𝘱𝘦𝘯 𝘋𝘢𝘵𝘢 𝘙𝘦𝘮𝘰𝘵𝘦 𝘚𝘦𝘯𝘴𝘪𝘯𝘨 𝘚𝘢𝘵𝘦𝘭𝘭𝘪𝘵𝘦 𝘔𝘪𝘴𝘴𝘪𝘰𝘯𝘴 𝘧𝘰𝘳 𝘓𝘢𝘯𝘥 𝘔𝘰𝘯𝘪𝘵𝘰𝘳𝘪𝘯𝘨 𝘢𝘯𝘥 𝘊𝘰𝘯𝘴𝘦𝘳𝘷𝘢𝘵𝘪𝘰𝘯 (2020).

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Processing Tree Canopy Height with Sentinel Series and GEDI 1. **Multi-sensor Data Loading**: The script begins by loading data from various sources, including SAR imagery from Sentinel-1, optical imagery from Sentinel-2, elevation data from SRTM, and land cover data from ESA World Cover. Integrating multi-sensor data provides rich environmental information, enabling more comprehensive analysis. 2. **Data Preparation**: Data from various sources are prepared with various operations, including filtering to remove disturbances such as clouds and shadows, image merging, and reprojection to ensure consistency and projection suitability. 3. **Training Dataset Preparation**: The training dataset is intelligently prepared by selecting point samples from GEDI data, which provides information on tree canopy height. Relevant predictor attributes, such as radar intensity, optical reflectance, elevation, and slope, are extracted from other images and included in the dataset. 4. **Regression Modeling with Random Forest Algorithm**: To model tree canopy height, the Random Forest Classifier (RFC) algorithm, which has proven effective in regression problems, is used. This model is trained using the previously prepared training dataset, and parameters such as the number of trees and maximum depth are set to improve model performance. 5. **Model Evaluation**: To evaluate model performance, validation is performed using a dataset that has never been seen before. Root Mean Squared Error (RMSE) is used as the evaluation metric, measuring how well the model fits observation data. Evaluation results, including RMSE for both the training and validation datasets, are presented to provide a clear understanding of model accuracy. 6. **Exporting Results**: After the model is assessed satisfactorily, the resulting regression images are exported for further analysis or integration with other platforms, enabling stakeholders to make informed decisions about environmental management. "Using multi-sensor data from Sentinel-1, Sentinel-2, SRTM, and GEDI, we attempted to model tree canopy height. After undergoing training and validation, the model yielded a Training RMSE of 5.22 and Validation RMSE of 6.38." #RemoteSensing #MachineLearning #EnvironmentalScience #GEE

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - Chapter highlights The topic of spatial indexing, also the topic of chapter five, has lots of really cool methods that make computing, especially with larger geospatial data sets, a lot more efficient. Learn more below! 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 48. Creating a Simple Spatial Index in GeoPandas 49. Using Simple Spatial Indexes for Efficient Queries 50. Efficient Spatial Indexing with RTree 51. Creating a Square Grid from Scratch 52. Visualizing RTree Indexing 53. Enclosing Grid Cell Identification Using RTree 54. Introduction to H3 Indexing 55. Visualizing H3 Grids Get your copy: 𝐀𝐦𝐚𝐳𝐨𝐧: https://lnkd.in/dUkHadCW 𝐒𝐡𝐨𝐩𝐢𝐟𝐲: https://lnkd.in/drsKuxWG 𝐀𝐩𝐩𝐥𝐞 𝐁𝐨𝐨𝐤𝐬: https://lnkd.in/dv9ejwmS 𝐁𝐚𝐫𝐧𝐞𝐬 & 𝐍𝐨𝐛𝐥𝐞: https://lnkd.in/dwh5p94V #geodatascience101 #geospatialessentials #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

Data Management! Discover the #Daarwin ecosystem and its ability to process various inputs, allowing DAARWIN to enabling it to effectively interpret the information they contain. Remember, our #GRIS platform provided access to over 6 000 000 public boreholes. Go to Gris: https://lnkd.in/eCcaNDQw #saalggeomechanics #digitalconstrution #backanalysis #AI #daarwin #datadrivendecisions #optimizeresources #ConstructionInnovation #geotechnicalengineering #design #Research #Technology #software #monitoringdata #machinelearning #civilengineer #geomechanics #civilengineer #plaxis #geotechnicaldata #geotechnical #safeconstruction #efficiencyandsafety #safeconstruction #EfficiencyInConstruction #EIC

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - Chapter highlights The topic of spatial indexing, also the topic of chapter five, has lots of really cool methods that make computing, especially with larger geospatial data sets, a lot more efficient. Have a sneak peek now ➡️➡️➡️ Section outline: 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 48. Creating a Simple Spatial Index in GeoPandas 49. Using Simple Spatial Indexes for Efficient Queries 50. Efficient Spatial Indexing with RTree 51. Creating a Square Grid from Scratch 52. Visualizing RTree Indexing 53. Enclosing Grid Cell Identification Using RTree 54. Introduction to H3 Indexing 55. Visualizing H3 Grids Get your copy: 𝐀𝐦𝐚𝐳𝐨𝐧: https://lnkd.in/dUkHadCW 𝐒𝐡𝐨𝐩𝐢𝐟𝐲: https://lnkd.in/drsKuxWG 𝐀𝐩𝐩𝐥𝐞 𝐁𝐨𝐨𝐤𝐬: https://lnkd.in/dv9ejwmS 𝐁𝐚𝐫𝐧𝐞𝐬 & 𝐍𝐨𝐛𝐥𝐞: https://lnkd.in/dwh5p94V #geodatascience101 #geospatialessentials #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization