Complex Event Detection (CED) in video streams involves numerous challenges such as object detection, tracking, spatio–temporal relationship identification, and event matching, which are often complicated by environmental variations, occlusions, and tracking losses. This systematic review presents an analysis of CED methods for video streams described in publications from 2012 to 2024, focusing on their effectiveness in addressing key challenges and identifying trends, research gaps, and future directions. A total of 92 studies were categorized into four main groups: training-based methods, object detection and spatio–temporal matching, multi-source solutions, and others. Each method’s strengths, limitations, and applicability are discussed, providing an in-depth evaluation of their capabilities to support real-time video analysis and live camera feed applications. This review highlights the increasing demand for advanced CED techniques in sectors like security, safety, and surveillance and outlines the key opportunities for future research in this evolving field.

Accurate reporting of annual site-level methane emissions is increasingly required under emerging regulatory and voluntary frameworks in the oil and gas (O&G) sector. In this study, we present an event-based framework for estimating annual methane emissions from upstream O&G facilities. The framework applies the Emission Event Data Model (EEDM) to spatiotemporally aggregate multi-scale emissions data into discrete events using the concept of Allen’s interval algebra and spatial proximity. Following the creation of events, emissions are categorized into three groups: resolved (with a known emission rate and duration), partially resolved (with a known emission rate but an unknown duration), and unresolved (with an unknown emission rate and duration) to facilitate various management and emissions estimation approaches. To estimate emissions and its associated uncertainties using events, we developed three Monte Carlo-based approaches, which are (1) estimating durations for partially resolved events using null detections and leak generation and natural repair processes; (2) estimating emissions from unresolved events based on the minimum detection limit of deployed technologies; and (3) estimating emissions from unresolved events using probabilistic occurrence and best-fit rate and duration distributions. This framework enables emissions to be reported and verified at a uniform scale rather than at the individual observation scale. To demonstrate the estimation of emissions using this framework, we created two case studies. In both studies, we performed emissions estimation using emission observations synthesized from real emissions data from an upstream O&G site. The proposed framework and methodologies can be implemented in voluntary initiatives such as Veritas 2.0 and the Oil & Gas Methane Partnership 2.0 and the event data model can be applied as a data management framework for the Measurement, Monitoring, Reporting, and Verification (MMRV) framework.

Complex event detection (CED) adds value to camera stream data in various applications such as workplace safety, task monitoring, security, and health. Recent CED frameworks have addressed the issues of limited spatiotemporal labels and costly training by decomposing the CED into low-level features, as well as spatial and temporal relationship extraction. However, these frameworks suffer from high resource costs, low scalability, and an increased number of false positives and false negatives. This paper proposes GICEDCAM, which distributes CED across edge, stateless, and stateful layers to improve scalability and reduce computation cost. Additionally, we introduce a Spatial Event Corrector component that leverages geospatial data analysis to minimize false negatives and false positives in spatial event detection. We evaluate GICEDCAM on 16 camera streams covering four complex events. Relative to a strong open-source baseline configured for our setting, GICEDCAM reduces end-to-end latency by 36% and total computational cost by 45%, with the advantage widening as objects per frame increase. Among corrector variants, Bayesian Network (BN) yields the lowest latency, Long Short-Term Memory (LSTM) achieves the highest accuracy, and trajectory analysis offers the best accuracy–latency trade-off for this architecture.

Heterogeneous gridded environmental datasets are increasingly used in Earth system modeling, atmospheric studies, and spatial analytics, but differences in coordinate reference systems, spatial resolutions, sector classifications, reporting units, and data formats hinder interoperability and cross-dataset comparison. Conventional latitude–longitude grids further introduce unequal cell areas that bias aggregation and regional comparison. This study presents a framework for harmonizing heterogeneous gridded datasets within a Discrete Global Grid System (DGGS), using explicit grid-selection criteria, area-weighted mass-preserving conversion, cross-scheme sector mapping, and five measurable evaluation criteria. Using methane as a demanding case, we transformed 13 open-access inventories from global to subnational scales into the rearranged Hierarchical Equal-Area isoLatitude Pixelization (rHEALPix) DGGS, harmonizing 324 source variables from five classification schemes and standardizing values to megagrams per year per cell. Conversion preserved emission totals, with negligible differences for continental, national, and regional inventories and differences within 2% to 3% for global inventories. Cell area is constant by construction, aggregation along the hierarchy is exact, and inventories at different resolutions can be compared after aggregation. The framework is reproducible and extensible to other gridded spatial data.