Big Earth Data

📢We're delighted to announce a new special issue: lnkd.in/gKesFKT9. If you are engaged in this dynamic field, we warmly invite you to contribute your valuable insights!
#remotesensing #earthobservation #ArtificialIntelligence #ai #MachineLearning #Geospatial #DataScience #SustainableDevelopment

deeptime: an R package that facilitates highly customizable and reproducible visualizations of data over geological time intervals Data visualization is a key component of any scientific data analysis workflow and is vital for the summarization and dissemination of complex ideas and results. One common hurdle across the Earth ...

📢deeptime: an #R package that facilitates highly customizable and reproducible visualizations of data over geological time intervals by William Gearty @willgearty.bsky.social
👉https://doi.org/10.1080/20964471.2025.2537516
#Datavisualization #reproducibility #opensource #paleontology #geology

It's so great to see this package (on CRAN since 2021) finally formally described in a journal! Here's to many more years of standardized, customizable, and reproducible geology data visualization!

And shout out to @richardstockey.bsky.social and @lewisajones.bsky.social for years of encouragement!

A novel ensemble model for multi-temporal forest vegetation classification: integrating spectral-temporal features and topographic constraints Understanding species distribution in large forest ecosystems is fundamental for biodiversity conservation, biomass estimation, climate regulation, soil and water conservation. While remote sensing...

📢 [Research Article] A novel ensemble model for multi-temporal forest vegetation classification: integrating spectral-temporal features and topographic constraints
👉Article link: doi.org/10.1080/2096...
💌 #Sentinel1 #Sentinel2 #deeplearning #remotesensing #landuse #landcover #forestmapping

Harnessing generative AI for enhanced disaster management: a systematic review In the consistently evolving artificial intelligence (AI) and large language models (LLMs), many organizations adopt these technologies’ capabilities to solve and assist core operations in many ind...

📢 [Review Article] Harnessing generative AI for enhanced disaster management: a systematic review
👉Article link: doi.org/10.1080/2096...

#Artificialintelligence #disastermanagement #riskmanagement #largelanguagemodel #bigearthdata #digitalearth #geoscience #remotesensing #GIS #risk

📢Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets by Alysha van Duynhoven & Suzana Dragićević
👉https://doi.org/10.1080/20964471.2025.2518763
#machinelearning #landcover #AI #GeoAI #remotesensing #earthobservation #GIS

Plot of global benthic δ18O data for 0 – 5.3 Ma (Lisiecki & Raymo, 2005) with geomagnetic polarity subchrons displayed on the top x-axis and planktic foraminiferal primary biozones plotted on the bottom x-axis using the deeptime package.

A mammal phylogeny (Garland et al., 1992) plotted in the fan layout using the ggtree and deeptime packages. The greyscale concentric circles in the background indicate geological stages, whereas the linear colored timescale indicates geological epochs.

Early tetrapod occurrence data (Jones et al., 2023) plotted as a taxonomic/biostratigraphic range plot using the geom_points_range() function from the deeptime package.

A stratigraphic column of Cretaceous lithostratigraphic units from the San Juan Basin, USA. The deeptime package has been used to add pattern fill which indicate the primary lithologies of the units as reported by the Macrostrat API (Peters et al., 2018) via the rmacrostrat R package (Jones et al., 2024).

📢 deeptime: an R package that facilitates highly customizable and reproducible visualizations of data over geological time intervals

🔗 doi.org/10.1080/2096...

Fully #openaccess in @bigearthdata1.bsky.social with insight about deeptime📦 development and code examples!

#rstats #geology #paleontology

📢Simulation of lake underwater terrain based on the XGBoost model: a case study of typical lakes on the #TibetanPlateau
👉https://doi.org/10.1080/20964471.2025.2515713
#lake #underwater #terrain #XGBoost #topography #DEM #waterstorage #bathymetry #climatechange #hydrology #3D #remotesensing #GIS

📢DACIA5: a #Sentinel-1 and #Sentinel-2 #dataset for agricultural #crop identification applications by A. Băicoianu, I. C. Plajer, M. Debu, et al.
👉Article link: doi.org/10.1080/2096...
💌 #Artificialintelligence #agriculture #smartagriculture #remotesensing #machinelearning #datasharing #datapaper

📢[Research Article] Modeling #deforestation drivers in the Brazilian #Amazon: a comparison of quantitative approaches by Alisson Castro Barreto, Tailon Martins & Adriano Mendonça Souza
👉https://doi.org/10.1080/20964471.2025.2510770
#biome #geoscience #GIS #remotesensing #Brazil #statisticalmethod

📢GeoFactory: an #LLM performance enhancement framework for geoscience factual and inferential tasks
👉https://doi.org/10.1080/20964471.2025.2506291
💌A guidance for adapting LLMs to #geoscience applications and paves the way for future multimodal implementations. Largelanguagemodel

📢 Daily-scale dataset of highly dynamic water of Poyang Lake
👉Article link: doi.org/10.1080/2096...
💌 #Remotelysensing #PoyangLake #randomforest #hydrology #landcover #opticalimagery #SAR #WaterIndex

📢 Classification of oil palm tree conditions from #UAV imagery using the #YOLO object detector by Aakash Thapa, Teerayut Horanont et al.
👉Article link: doi.org/10.1080/2096...
💌 #Oilpalmtree #deeplearning #YOLOv8 #YOLO #objectdetection #precisionagriculture #remotesensing

Detection of drainage ditches from LiDAR DTM using U-Net and transfer learning Accurate mapping of ditches is essential for effective hydrological modeling and land management. Traditional methods, such as manual digitization or threshold-based extraction, utilize LiDAR-deriv...

📢[Research Article] Detection of #drainage ditches from #LiDAR #DTM using U-Net and transfer learning by Holger Virro, Alexander Kmoch, et al.
👉Article link: doi.org/10.1080/2096...
💌 #remotesensing #hydrology

📢A deep learning pipeline to #powerinfrastructure detection in #highresolution satellite images by Mengqi Ye et al.
👉https://doi.org/10.1080/20964471.2025.2490408
💌It highlights the potential of #deeplearning for large-scale #power grid mapping using from #Worldview-3 imagery.
#remotesensing

📢 [New Article] A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China
👉Article link: doi.org/10.1080/2096...
Study uses #Landsat data to map #Artemia in China’s #EbinurLake, achieving 94.5% accuracy. #RemoteSensing #BrineShrimp #Conservation

📢 Enhancing geodatabases operability: advanced human-computer interaction through #RAG and Multi-Agent Systems
👉Article link: doi.org/10.1080/2096...
#LargeLanguageModel #LLM #MultiAgentSystem #SQL #geospatialdatabases #GeoAI #GIS #dataquery #datamanagement #geodatabase

📢 [Review Article] Exploring the concept of digital twins of wetlands for supporting ecosystem monitoring and management by Bing Lu et al.
👉Article link: doi.org/10.1080/2096...
💌 #Wetland #remotesensing #GIS #ecosystem #digitaltwins

📢Li-GS: a fast #3D Gaussian reconstruction method assisted by LiDAR point clouds
👉Article link: doi.org/10.1080/2096...
💌Li-GS uses #LiDAR #pointclouds and dynamic voxel filtering to accelerate training while improving #geometric accuracy and reducing hardware demands.
#remotesensing #digitaltwin

📢[Data Note] Restoration of missing #oceancolor data in high-latitude oceans using neural network model
👉https://doi.org/10.1080/20964471.2025.2474655
💌The NN-LAT50 #dataset for #highlatitude #oceans significantly improves retrieval accuracy and winter coverage. #polar #remotesensing #oceanography

🔥 This Wednesday, I reached a major milestone: I successfully completed my #PhD journey by defending my thesis ‘Advancing #Forest Monitoring using #Sentinel1 C-band #SAR Time Series and #MachineLearning Approaches' at the @sciencecharles.bsky.social @charlesuni.cuni.cz
Details on my thesis 👇

📢Interactive Earth system data cube visualization in Jupyter notebooks by Maximilian Söchting (@soechting.bsky.social), Miguel D. Mahecha (@miguelmahecha.bsky.social) et al.
👉https://doi.org/10.1080/20964471.2025.2471646
#opensource #3D #datacube #visualization #Jupyter #geoscience #remotesensing

👍👍👍

Excited to share the latest paper emerging from the fantastic PhD thesis of @soechting.bsky.social! Interactive Earth System Data Cube visualization in Jupyter Notebooks! www.tandfonline.com/doi/full/10....

📢Mapping #sugarcane plantations in Northeast #Thailand using multi-temporal data from multi-sensors and #machinelearning algorithms by Savittri Ratanopad Suwanlee, Jaturong Som-Ard et al.
👉https://doi.org/10.1080/20964471.2025.2463730
#remotesensing #earthobservation #agriculture #cropmapping

STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery Flooding is a major global natural disaster exacerbated by climate change and urbanization. Timely assessment and mapping of inundations are crucial for preventive and emergency measures, driving t...

📢 STURM-Flood a curated #dataset for deep learning-based #flood extent #mapping leveraging #Sentinel-1 and #Sentinel-2 imagery by Nicla Notarangelo et al.
👉https://doi.org/10.1080/20964471.2025.2458714
#EarthObservation #ClimateResilience #RemoteSensing #OpenData #AI #disaster #risk