Integrating large language models (LLMs) into autonomous driving motion planning has recently emerged as a promising direction, offering enhanced interpretability, better controllability, and improved generalization in rare and long-tail scenarios. However, existing methods often rely on abstracted perception or map-based inputs, missing crucial visual context, such as fine-grained road cues, accident aftermath, or unexpected obstacles, which are essential for robust decision-making in complex driving environments. To bridge this gap, we propose VLMPlanner, a hybrid framework that combines a learning-based real-time planner with a vision-language model (VLM) capable of reasoning over raw images. The VLM processes multi-view images to capture rich, detailed visual information and leverages its common-sense reasoning capabilities to guide the real-time planner in generating robust and safe trajectories. Furthermore, we develop the Context-Adaptive Inference Gate (CAI-Gate) mechanism that enables the VLM to mimic human driving behavior by dynamically adjusting its inference frequency based on scene complexity, thereby achieving an optimal balance between planning performance and computational efficiency. We evaluate our approach on the large-scale, challenging nuPlan benchmark, with comprehensive experimental results demonstrating superior planning performance in scenarios with intricate road conditions and dynamic elements.

Our paper proposes a direct sparse visual odometry method that combines event and RGBD data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors. Event cameras offer high temporal resolution and dynamic range, which can eliminate the issue of blurred RGB images during fast movements. This unique strength holds a potential for accurate pose estimation of agile- legged robots, which has been a challenging problem to tackle. Our framework leverages the benefits of both RGBD and event cameras to achieve robust and accurate pose estimation, even during dynamic maneuvers such as jumping and landing a quadruped robot, the Mini-Cheetah. Our major contributions are threefold: Firstly, we introduce an adaptive time surface (ATS) method that addresses the whiteout and blackout issue in conventional time surfaces by formulating pixel-wise decay rates based on scene complexity and motion speed. Secondly, we develop an effective pixel selection method that directly samples from event data and applies sample filtering through ATS, enabling us to pick pixels on distinct features. Lastly, we propose a nonlinear pose optimization formula that simultaneously performs 3D-2D alignment on both RGB-based and event-based maps and images, allowing the algorithm to fully exploit the benefits of both data streams. We extensively evaluate the performance of our framework on both the public dataset and our own quadruped robot dataset, demonstrating its effectiveness in accurately estimating the pose of agile robots during dynamic movements. Supplemental video: https://youtu.be/-5ieQShOg3M

Long-term Landsat time series imagery provides valuable opportunities for monitoring land surface changes. However, missing observations that result from clouds, cloud shadows, and scan line corrector failure makes Landsat data record neither a continuous nor a consistent time series. In this study, we present an approach to produce a gap-free Landsat time series for the Taita Hills in southeastern Kenya. We used simulated gaps in nearly cloud-free images to assess the performance of the approach while considering two factors: the size of the gaps and effect of the wet or dry seasons. It turned out that filling image with the largest area of simulated gaps, which were equivalent to 52% of the test image area, yielded almost the same root mean square error, relative RMSE and the coefficient of determination as the smallest gaps equivalent to 25%. Furthermore, gap-free images in both dry and wet seasons could be filled without visual artifacts, and dry season images were better predicted. Finally, the time series images produced for the study area showed consistent temporal variation across land use/land cover types.

Inexpensive and highly capable unmanned aerial vehicles (aka drones) have enabled people to contribute high-quality videos at a global scale. However, a key challenge exists for accepting videos from untrusted sources: establishing when a particular video was taken. Once a video has been received or posted publicly, it is evident that the video was created before that time, but there are no current methods for establishing how long it was made before that time.

A Semi Random Circle routing for mobile Sink joint Ray Routing for data (SRCRR) scheme is proposed for preserving sink-location privacy with a delay guaranteed. In the SRCRR scheme, the data are directionally routed along ray paths and stored at intermediate nodes probabilistically. The Sink moves in a semirandom circular pattern to collect data from the local nodes occasionally, which guarantees that the data will be collected with an acceptable delay and prevents attackers from predicting their locations and movements. The experimental results indicate that the performance of the SRCRR scheme is better than that of the previous schemes.

As the most commonly techniques to landslide inventory mapping, visual interpretation and geomorphological field surveys are time-consuming and labor-intensive. In this paper, a probabilistic topic model, maximum entropy discrimination latent Dirichlet allocation (MedLDA), is presented to detect landslides with satellite images of two different spatial resolutions in a weakly supervised way. A two-stage algorithm is inferred the model. First, before- and after- the event MODIS NDVI productions are employed to roughly locate probable landslides, i.e., low-resolution vegetation-cover changes. Second, MedLDA model is learned by both NDVI change values (i.e., the weakly supervised information) and post-event SPOT 5 images to detect the landslide. Experimental results demonstrate that the proposed method is a very promising way to detect landslides in vegetated regions.

In this paper we introduce an object-based change detection model using correlation analysis and classification. First we use eCognition to obtain an over-segmentation map. Then linear regression is used to gain three unique types of parameters - regression coefficient, offset, and correlation coefficient which can provide valuable information about the location and numeric change value derived within the segmentation objects in the two data sets. Understandably, the two data tend to be highly correlated when little change occurs, and uncorrelated when change occurs. Then we treat the three variables as a three-band image. Finally, we perform maximum likelihood classification with training examples. The result shows that our method performs better than the methods proposed by Yang [2] and J. Im [3]. The advantages of our method are that it performs automatically without selecting threshold empirically and alleviates the “salt and pepper” effect.