GIS and DWR based short-term and impending landslide forecasting for Liangshan prefecture (China)

Tema rada je predviđanje odrona zemlje za mala i srednje velika područja. Izabrana je autonomna prefektura Liangshan Yi u pokrajini Sichuan (Kina), kao istraživano područje. Geographic Information System (GIS) i Doppler Weather Radar (DWR) su rabljene kao tehnologije. Osam utjecajnih elemenata uključujući nagib padine, stratigrafsku litologiju, upotrebu zemlje, količinu padavina, intenzitet padavina po satu te udaljenost od najbližeg rasjeda, rijeke i ceste su prediktori. Information Model (IM), Fuzzy Mathematics (FM), i Extenics su teorijska podrška u ovom radu, u kojem se gradi model za predviđanje odrona u malim i srednje velikim područjima te razvija sustav za predviđanje mogućih, skorih odrona, zasnovan na GIS i DWR, na platformi ArcGIS 9.3 prefekture Liangshan. Sustavom se predviđa odron za prefekturu Liangshan s intervalom od 1 sata i vremenom predviđanja od 3 sata. Rezultati simulacije pokazuju da sustav može dobro poslužiti za predviđanje odrona u malim i srednje velikim područjima te je stoga primjenjiv za predviđanje opasnosti na razini prefekture.Landslide forecasting for small and medium sized regions is taken as the subject matter. Liangshan Yi Autonomous Prefecture in Sichuan Province (China) as the study area. Geographic Information System (GIS) and Doppler Weather Radar (DWR) were used as the technologies. Eight influencing elements including slope gradient, stratigraphic lithology, land use, amount of precipitation, intensity of hourly precipitation, and proximity to the nearest fault, river, and road are the predictors. Information Model (IM), Fuzzy Mathematics (FM), and Extenics are the theoretical support in this paper, which builds a landslide forecasting model for small and medium sized regions and develops a GIS and DWR based short-term and impending landslide forecasting system on the ArcGIS 9.3 Platform for Liangshan Prefecture. The system provides a seamless rolling landslide forecast for Liangshan Prefecture, with a 1 h interval and a 3 h forecast period. The simulation results indicate that the system serves well in landslide forecasting for small and medium sized regions, and is thus applicable in the hazard forecasting practice at prefecture scale

milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

Approaching the era of ubiquitous computing, human motion sensing plays a crucial role in smart systems for decision making, user interaction, and personalized services. Extensive research has been conducted on human tracking, pose estimation, gesture recognition, and activity recognition, which are predominantly based on cameras in traditional methods. However, the intrusive nature of cameras limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose \textit{milliFlow}, a novel deep learning method for scene flow estimation as a complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method with an average 3D endpoint error of 4.6cm, significantly surpassing the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition, human parsing, and human body part tracking. To foster further research in this area, we provide our codebase and dataset for open access.Comment: 15 pages, 8 figure

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Self-Supervised Scene Flow Estimation with 4-D Automotive Radar

Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popular automotive sensor for its robustness against adverse weather and lighting conditions. Compared with the LiDAR point clouds, radar data are drastically sparser, noisier and in much lower resolution. Annotated datasets for radar scene flow are also in absence and costly to acquire in the real world. These factors jointly pose the radar scene flow estimation as a challenging problem. This work aims to address the above challenges and estimate scene flow from 4-D radar point clouds by leveraging self-supervised learning. A robust scene flow estimation architecture and three novel losses are bespoken designed to cope with intractable radar data. Real-world experimental results validate that our method is able to robustly estimate the radar scene flow in the wild and effectively supports the downstream task of motion segmentation.Comment: Copyright (c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work