A5087996857- Distributed and Parallel Computing Systems
- Geographic Information Systems Studies
- Textile materials and evaluations
- Scientific Computing and Data Management
- Advanced Computational Techniques and Applications
- Real-Time Systems Scheduling
- Remote Sensing in Agriculture
- demographic modeling and climate adaptation
- Education Practices and Evaluation
- Embedded Systems Design Techniques
- Data Visualization and Analytics
- Remote Sensing and Land Use
- Remote-Sensing Image Classification
- Species Distribution and Climate Change
- Environmental Monitoring and Data Management
- Computational Physics and Python Applications
- Software Engineering and Design Patterns
- Opportunistic and Delay-Tolerant Networks
- Big Data Technologies and Applications
- Plant Water Relations and Carbon Dynamics
- Time Series Analysis and Forecasting
Leipzig University
2023-2025
Center For Remote Sensing (United States)
2024
Abstract Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led a high demand for catalogues and tools their computation. However, most these resources either closed-source, outdated, unconnected catalogue or lacking common Application Programming Interface (API). Here we present “Awesome Indices” (ASI), standardized...
Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics responses climatic extremes, yet the data complexity can challenge effectiveness of machine models. Despite recent progress in deep monitoring, there is a...
Terrestrial evaporation (E) is a critical climate variable that links the water, carbon, and energy cycles. It plays vital role in regulating precipitation, temperature, extreme events such as droughts, floods, heatwaves. In hydrology, E represents net loss of water resources, while agriculture, it determines irrigation demands. Despite its significance, global estimates remain uncertain due to scarcity ground-based measurements, complexity physiological atmospheric interactions, challenges...
Advancements in Earth system science have seen a surge diverse datasets. System Data Cubes (ESDCs) been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature unlocks significant opportunities Artificial Intelligence (AI) applications. By providing well-organised data, are ideally suited wide range sophisticated AI-driven tasks. An...
Abstract Recent advancements in Earth system science have been marked by the exponential increase availability of diverse, multivariate datasets characterised moderate to high spatio-temporal resolutions. System Data Cubes (ESDCs) emerged as one suitable solution for transforming this flood data into a simple yet robust structure. ESDCs achieve organising an analysis-ready format aligned with grid, facilitating user-friendly analysis and diminishing need extensive technical processing...
Progress in Earth system science is accelerating rapidly, due to the increasing availability of multivariate datasets, often global, with moderate high spatio-temporal resolutions. Turning these data into knowledge presents interoperability, technical, analytical, and other challenges. System Data Cubes (ESDCs) have surfaced as essential tools, offering analysis-ready, cloud-optimised solutions. Coupled advancements Artificial Intelligence (AI), solutions potential release a wealth...
Many subsystems of Earth are constantly monitored in space and time undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due complexity big data generated by models Observation (EO). To overcome barrier, we present Leipzig Explorer Data Cubes (lexcubeorg), an interactive visualization that allows users explore terabyte-scale sets with minimal latency through space, time, variables, model variants. With over 2...
Data streams representing the Earth system both through modeling and remote sensing approaches, encompass a diverse range massive amount of information. Unveiling insights at global local scales becomes increasingly challenging for wider public broader scientific audience as temporal spatial resolutions data sets continually improve. An effective solution to this involves development fully interactive visualizations capable rendering terabytes in real-time, spanning time, space, variables,...
Recent advancements in Earth system science have been marked by the exponential increase availability of diverse, multivariate datasets characterised moderate to high spatio-temporal resolutions. System Data Cubes (ESDCs) emerged as one suitable solution for transforming this flood data into a simple yet robust structure. ESDCs achieve organising an analysis-ready format aligned with grid, facilitating user-friendly analysis and diminishing need extensive technical processing knowledge....
A variety of Earth system data streams are being captured and derived from remote sensing observations modelling approaches. Since the spatial temporal resolutions these datasets continuously rise, global local insights become more difficult to obtain only specialists able effectively access explore data.Here we present Leipzig Explorer Data Cubes (lexcube.org), first fully interactive viewer for large cubes, enabling exploration visualization terabytes through space time. Lexcube runs in...