VideoARD: An Analysis-Ready Multi-Level Data Model for Remote Sensing Video.
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| Title: | VideoARD: An Analysis-Ready Multi-Level Data Model for Remote Sensing Video. |
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| Authors: | Wu, Yang1 (AUTHOR), Zhang, Chenxiao2 (AUTHOR), Lu, Yang1,3 (AUTHOR), Su, Yaofeng1,4 (AUTHOR), Jiang, Xuping1 (AUTHOR), Xiang, Zhigang2,3 (AUTHOR), Li, Zilong1,2,3,4 (AUTHOR) lizilong@whu.edu.cn |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 22, p3746. 35p. |
| Subjects: | Remote sensing, Data modeling, Data analysis, Motion analysis, Metadata, Multisensor data fusion, Data management |
| Abstract: | Highlights: What are the main findings? A multi-level VideoARD model formalizes scene–object–event entities with standardized metadata and provenance. A spatiotemporal VideoCube links frame facts to spatial, temporal, product, quality, and semantic dimensions to enable OLAP-style queries and cross-sensor integration. What is the implication of the main finding? The standardized representation streamlines workflows, reducing preprocessing burden and improving reproducibility for detection, tracking, and event analysis. Benchmarks show lower query latency and resource usage with non-inferior or slightly improved task accuracy versus frame-level baselines. Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operational use of video streams is hindered by very large data volumes, heterogeneous acquisition platforms, inconsistent preprocessing practices, and the absence of standardized formats that deliver data ready for immediate analysis. These shortcomings force repeated low-level computation, complicate semantic extraction, and limit reproducibility and cross-sensor integration. This manuscript presents a principled multi-level analysis-ready data (ARD) model for remote sensing video, named VideoARD, along with VideoCube, a spatiotemporal management and query infrastructure that implements and operationalizes the model. VideoARD formalizes semantic abstraction at scene, object, and event levels and defines minimum and optimal readiness configurations for each level. The proposed pipeline applies stabilization, georeferencing, key frame selection, object detection, trajectory tracking, event inference, and entity materialization. VideoCube places the resulting entities into a five-dimensional structure indexed by spatial, temporal, product, quality, and semantic dimension, and supports earth observation OLAP-style operations to enable efficient slicing, aggregation, and drill down. Benchmark experiments and three application studies, covering vessel speed monitoring, wildfire detection, and near-real-time three-dimensional reconstruction, quantify system performance and operational utility. Results show that the proposed approach achieves multi-gigabyte-per-second ingestion under parallel feeds, sub-second scene retrieval for typical queries, and second-scale trajectory reconstruction for short tracks. Case studies demonstrate faster alert generation, improved detection consistency, and substantial reductions in preprocessing and manual selection work compared with on-demand baselines. The principal trade-off is an upfront cost for materialization and storage that becomes economical when queries are repeated or entities are reused. The contribution of this work lies in extending the analysis-ready data concept from static imagery to continuous video streams and in delivering a practical, scalable architecture that links semantic abstraction to high-performance spatiotemporal management, thereby improving responsiveness, reproducibility, and cross-sensor analysis for Earth observation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A multi-level VideoARD model formalizes scene–object–event entities with standardized metadata and provenance. A spatiotemporal VideoCube links frame facts to spatial, temporal, product, quality, and semantic dimensions to enable OLAP-style queries and cross-sensor integration. What is the implication of the main finding? The standardized representation streamlines workflows, reducing preprocessing burden and improving reproducibility for detection, tracking, and event analysis. Benchmarks show lower query latency and resource usage with non-inferior or slightly improved task accuracy versus frame-level baselines. Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operational use of video streams is hindered by very large data volumes, heterogeneous acquisition platforms, inconsistent preprocessing practices, and the absence of standardized formats that deliver data ready for immediate analysis. These shortcomings force repeated low-level computation, complicate semantic extraction, and limit reproducibility and cross-sensor integration. This manuscript presents a principled multi-level analysis-ready data (ARD) model for remote sensing video, named VideoARD, along with VideoCube, a spatiotemporal management and query infrastructure that implements and operationalizes the model. VideoARD formalizes semantic abstraction at scene, object, and event levels and defines minimum and optimal readiness configurations for each level. The proposed pipeline applies stabilization, georeferencing, key frame selection, object detection, trajectory tracking, event inference, and entity materialization. VideoCube places the resulting entities into a five-dimensional structure indexed by spatial, temporal, product, quality, and semantic dimension, and supports earth observation OLAP-style operations to enable efficient slicing, aggregation, and drill down. Benchmark experiments and three application studies, covering vessel speed monitoring, wildfire detection, and near-real-time three-dimensional reconstruction, quantify system performance and operational utility. Results show that the proposed approach achieves multi-gigabyte-per-second ingestion under parallel feeds, sub-second scene retrieval for typical queries, and second-scale trajectory reconstruction for short tracks. Case studies demonstrate faster alert generation, improved detection consistency, and substantial reductions in preprocessing and manual selection work compared with on-demand baselines. The principal trade-off is an upfront cost for materialization and storage that becomes economical when queries are repeated or entities are reused. The contribution of this work lies in extending the analysis-ready data concept from static imagery to continuous video streams and in delivering a practical, scalable architecture that links semantic abstraction to high-performance spatiotemporal management, thereby improving responsiveness, reproducibility, and cross-sensor analysis for Earth observation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17223746 |
