Lei Wang

Aircraft trajectory prediction supports trajectory-based operations and air traffic management by estimating future aircraft motion. This systematic review synthesizes studies on trajectory, position, and flight state prediction for civil, manned, fixed-wing aircraft during airborne operation from takeoff to touchdown. Following evidence assessment, 198 studies were retained as primary evidence, of which 55 met the predefined criteria for formal performance synthesis. The review uses a Preparation, Prediction, Update, and Output (PPUO) framework and organizes the evidence around modeling methodology, task formulation, and performance evaluation. The findings show that physics-based, state estimation, data-driven, and hybrid methods have different modeling assumptions and operating conditions, and the available evidence does not support any one paradigm as universally superior across tasks and operating scenarios. Reported performance is jointly influenced by prediction horizon, target cardinality, interaction context, and evaluation conditions. The evidence is concentrated on single-target prediction; quantitative evidence for joint multi-aircraft prediction and probabilistic uncertainty evaluation remains limited, while heterogeneity in experimental conditions restricts direct cross-study comparison. Future research should establish reproducible benchmarks with complete reporting of experimental conditions and conduct cross-scenario validation while evaluating trajectory accuracy, uncertainty quality, computational efficiency, and operational assurance together.

Low-altitude economy, fine-grained surveying, emergency response, and autonomous exploration are driving Unmanned Aerial Vehicles (UAVs) from passive data-acquisition platforms toward task-executing aerial agents. This capability transition requires UAVs to operate within complex, open 3D environments under six-degree-of-freedom (6DoF) motion, strict size, weight and power (SWaP) limits, partial observations, onboard computation constraints, and safety-critical action requirements. Therefore, the central scientific problem of UAV 3D scene understanding is how a UAV agent can construct, maintain, and use a spatiotemporally coherent and uncertainty-aware 3D scene state to support localization, planning, and safe interaction. Existing surveys mainly categorize the literature according to sensor types, application scenarios, or generic 3D representations, and thus provide limited analysis of how 3D scene understanding supports agent-capability evolution under embodied aerial constraints. To address this gap, we review UAV 3D scene understanding along an agent-capability evolution from offline interpretation to online understanding and predictive reasoning. This perspective highlights the underlying tensions between representation fidelity and onboard deployability, open-vocabulary semantic coverage and calibrated trustworthiness, post-flight static reconstruction and online scene-state maintenance, and predictive reasoning and safety-bounded decision support. Finally, we discuss open challenges in closed-loop data construction, trustworthy scene-state memory, collaborative fusion, sim-to-real transfer, and reliable onboard deployment.

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support.

The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs.

Traditional Multiple Object Tracking (MOT) methods in satellite videos mostly follow the Detection-Based Tracking (DBT) framework. However, the DBT framework assumes that all objects are correctly recognized and localized by the detector. In practice, the low resolution of satellite videos, small objects, and complex backgrounds inevitably leads to a decline in detector performance. To alleviate the impact of detector degradation on track, we propose Coarse-Fine Tracker, a framework that integrates the MOT framework with the Tracking Any Point (TAP) method CoTracker for the first time, leveraging TAP’s persistent point correspondence modeling to compensate for detector failures. In our Coarse-Fine Tracker, we divide the satellite video into sub-videos. For one sub-video, we first use ByteTrack to track the outputs of the detector, referred to as coarse tracking, which involves the Kalman filter and box-level motion features. Given the small size of objects in satellite videos, we treat each object as a point to be tracked. We then use CoTracker to track the center point of each object, referred to as fine tracking, by calculating the appearance feature similarity between each point and its neighboring points. Finally, the Consensus Fusion Strategy eliminates mismatched detections in coarse tracking results by checking their geometric consistency against fine tracking results and recovers missed objects via linear interpolation or linear fitting. This method is validated on the VISO and SAT-MTB datasets. Experimental results in VISO show that the tracker achieves a multi-object tracking accuracy (MOTA) of 66.9, a multi-object tracking precision (MOTP) of 64.1, and an IDF1 score of 77.8, surpassing the detector-only baseline by 11.1% in MOTA while reducing ID switches by 139. Comparative experiments with ByteTrack demonstrate the robustness of our tracking method when the performance of the detector deteriorates.