This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize tasks into five key frameworks—classification, detection, segmentation, sequence, and the hybrid framework—and analyze their specific applications landslide-related tasks. Following presented frameworks, we state-or-art studies provide clear insights powerful capability models for landslide mapping,...

Abstract Seismology is witnessing rapid growth in both the volume and variety of earthquake observational data, but current tools for effectively integrating these heterogeneous data remain limited. Here, we propose SafeNet, a scalable deep learning framework designed to address challenges through use multimodal fusion neural networks. SafeNet integrates 282-dimensional seismic indicators from catalogs, capturing long-, medium-, short-term patterns, associates activity with geological...

Partial face recognition (PFR) in unconstrained environment is a very important task, especially video surveillance, mobile devices, etc. However, few studies have tackled how to recognize an arbitrary patch of image. This study combines Fully Convolutional Network (FCN) with Sparse Representation Classification (SRC) propose novel partial approach, called Dynamic Feature Matching (DFM), address images regardless size. Based on DFM, we sliding loss optimize FCN by reducing the...

Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult interpret. We argue that translating each into graph-based semantic representation, i.e., scene graph, explicitly encodes objects relationships detected within image, would benefit representing describing images. To this end, we propose novel architecture for visual by modeling two-level on...

Landslides are a major geohazard that endangers human lives and properties. Recently, efforts have been made to use Synthetic Aperture Radar Interferometry (InSAR) for landslide monitoring. However, it is still difficult effectively automatically identify slow-moving landslides distributed over large area due phase unwrapping errors, decorrelation, troposphere turbulence computational requirements. In this study, we develop new approach combining phase-gradient stacking deep-learning network...

Abstract Visual simultaneous localization and mapping (vSLAM) is inherently constrained by the static world assumption, which renders success in presence of dynamic objects rather challenging. In this paper, we propose a real-time semantic vSLAM system designed for both indoor outdoor environments. By employing object detection, identify 80 categories utilize motion consistency checks to pinpoint outliers each image. Distinct methods are presented examining states humans other objects. For...

Abstract. Drought indices are crucial for assessing and managing water scarcity agricultural risks; however, the lack of a unified data foundation in existing datasets leads to inconsistencies that challenge comparability drought indices. This study is dedicated creating CHM_Drought, an innovative comprehensive long-term meteorological dataset with spatial resolution 0.1° collected from 1961 2022 mainland China. It features six pivotal indices: standardized precipitation index (SPI),...

With the ever-increasing growth of Internet, numerous copies documents become serious problem for search engine, opinion mining and many other web applications. Since partial-duplicates only contain a small piece text taken from sources most existing near-duplicate detection approaches focus on document level, partial duplicates can not be dealt with well. In this paper, we propose novel algorithm to realize partial-duplicate task. Besides similarities between documents, our proposed...

Person re-identification (ReID), which aims at matching individuals across non-overlapping cameras, has attracted much attention in the field of computer vision due to its research significance and potential applications. Triplet loss-based CNN models have been very successful for person ReID, optimize feature embedding space such that distances between samples with same identity are shorter than those different identities. Researchers found hard triplets' mining is crucial success triplet...

Near infrared (NIR) partial face images acquired in iris recognition less constrained environments contain plentiful identity information which have not been fully exploited. In this paper, a NIR (PFR) algorithm is designed according to the characteristics of systems. preprocessing stage, eye corners are utilized for alignment because regions usually visible. feature representation highly compact and discriminatory features extracted based on Multiscale Double Supervision Convolutional...

Document retrieval techniques form the foundation for development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute semantic similarity. However, such scalar similarity difficult reflect enough impedes our comprehension results. In addition, this computational process mainly emphasizes global semantics ignores fine-grained relationship between query complex text in document. paper, we propose new method called $\textbf{Ge}$neration...

Abstract Objective: Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as preprocessing step in clinical and analysis applications, such surgical planning, diagnostic assistance, navigation. We aim overcome these challenges: Deep learning-based methods often struggle with complex displacements lack effective interaction between global local feature information. They also neglect the spatial position...

Abstract. Precipitation is a critical driver of the water cycle, profoundly influencing resources, agricultural productivity, and natural disasters. However, existing gridded precipitation datasets exhibit markable deficiencies in capturing spatiotemporal physical correlations precipitation, which limits their accuracy, particularly regions with sparse meteorological stations. Therefore, this study proposes completely new generation scheme to address these issues. The long-term daily...

Atmospheric moisture plays a crucial role in connecting global water and energy exchanges within the cycle. Using recycling model, this study examines spatiotemporal characteristics of precipitation evaporation ratios (PRR ERR) across 200 river basins worldwide from 1980 to 2021, with data fused three reanalysis datasets. The results reveal that regions near equator exhibit higher PRR values, signifying strong self-sufficiency, whereas arid, high-latitude, inland show lower indicating...

Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less cross-subject tasks. Reconstructing high-quality images is a challenging problem due to profound individual differences between subjects and the scarcity of data annotation. In this work, we proposed MindTuner for decoding, which achieves rich semantic reconstructions using only 1 hour fMRI training benefiting phenomena fingerprint human system novel...

The vehicle detection and tracking are important tasks in intelligent transportation system. traditional methods of often cause the coarse-grained results due to suffering from complex environments. YOLO is a pragmatic approach multi-target with simple effective algorithm. This paper use detect moving vehicles modified Kalman filter algorithm dynamically track detected vehicles, achieving overall competitive performance day or night. experimental show method robust occluding congested roads...

The emergence of the Internet Things (IoT) has led to a remarkable increase in volume data generated at network edge. In order support real-time smart IoT applications, massive amounts from edge devices need be processed using methods such as deep neural networks (DNNs) with low latency. To improve application performance and minimize resource cost, enterprises have begun adopt Edge computing, computation paradigm that advocates processing input locally However, nodes are often...

Facial expression recognition (FER) task in the wild is challenging due to some uncertainties, such as ambiguity of facial expressions, subjective annotations, and low-quality images. A novel model for FER in-the-wild datasets proposed this study solve these uncertainties. The overview method follows. First, images are grouped into high low uncertainties by pre-trained network. graph convolutional network (GCN) framework then used with uncertainty obtain geometry cues, including relationship...