Abstract El Niño-Southern Oscillation (ENSO), which is one of the main drivers Earth’s inter-annual climate variability, often causes a wide range anomalies, and advance prediction ENSO always an important challenging scientific issue. Since unified complete theory has yet to be established, people use related indicators, such as Niño 3.4 index southern oscillation (SOI), predict development trends through appropriate numerical simulation models. However, because phenomenon highly complex...

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial temporal resolution remote sensing (RS) images for wide range of precision agriculture applications, which can help reduce costs environmental impacts by providing detailed agricultural information optimize field practices. Furthermore, deep learning (DL) has been successfully applied in applications such weed detection, crop pest disease etc. an intelligent tool....

Constructing a knowledge graph of geological hazards literature can facilitate the reuse and provide reference for hazard governance. Named entity recognition (NER), as core technology constructing graph, has to face challenges that named entities in are diverse form, ambiguous semantics, uncertain context. This introduce difficulties designing practical features during NER classification. To address above problem, this paper proposes deep learning-based model; namely, deep, multi-branch...

Object detection that focuses on locating objects of interest and categorizing them has long played a critical role in the development remote sensing imagery. Following significant improvements Earth observation technologies, high-resolution (HRRS) images show additional detailed information more complex patterns. Some applications, such as urban monitoring, military reconnaissance, national security, have urgent needs terms identifying small-scale (small) weak-feature-response (weak)...

Urban informal settlements (UIS) are high-density population areas with low urban infrastructure standards. UIS classification, which automates identifying UIS, is of great significance for various computing tasks. Fast and accurate extraction has the following difficulties. First, from a high-resolution perspective, buildings in settlement low-floor dense, complex spatial relationships. Second, settlements' remote sensing observation characteristics highly inconspicuous, caused by shooting...

Submeter high-resolution remote sensing image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning (DL)-based models have achieved remarkable performance in many tasks through end-to-end supervised learning. However, the excellent of DL-based relies heavily on a large number well-annotated samples, which is impossible practical scenarios. Additionally, training set contain all different types. To overcome these problems, this...

Urban informal settlements (UIS) are high-density population with low standards of living and supply. UIS semantic segmentation, which identifies pixels corresponding to in remote sensing images, is crucial the estimation poor communities, urban management, resource allocation, future planning, particularly megacities. However, most studies on settlement mapping either based parcels (image classification) or (semantic segmentation). Few utilize object information improve mapping. Since...

Dense time-series remote sensing images have transformed the traditional bitemporal land-cover change detection to continuous monitoring. Previous work mostly employs linear fitting, prediction, or decomposition methods, and accuracy is not high. The latest progress of deep learning (DL) shows its advantages in However, DL models are computationally expensive require lots labeled samples, resulting often employed prediction-threshold-based unsupervised method. determination a reasonable...

Mapping population distribution at fine spatial scales is significant and fundamental for resource utilization, assessment of city disaster, environmental regulation, urbanization. Multisource data produced by remote social sensing have been widely used to disaggregate census information map distributions resolution. However, it challenging achieve accurate high-spatial-resolution mapping combining multisource considering geographic heterogeneity. The existing approaches do not consider...

Due to the complex and highly heterogeneous land cover in urban areas, single-temporal pixel-wise parcel-wise classification cannot realize high-precision recognition of ground objects. Semantic segmentation satellite image time series (SITS), can distinguish objects with similar spectral reflection temporal evolution. But optical SITS have problems uneven time-frequency distribution incomplete, which makes it impossible directly use existing models carry out semantic segmentation. This...

High-resolution spatial distribution maps of GDP are essential for accurately analyzing economic development, industrial layout, and urbanization processes. However, the currently accessible gridded datasets limited in number resolution. Furthermore, high-resolution mapping remains a challenge due to complex sectoral structure GDP, which encompasses agriculture, industry, services. Meanwhile, multi-source data with high resolution can effectively reflect level regional development....

Since Landsat-1 first started to deliver volumes of pixels in 1972, the archived data remote sensing centers have increased continuously. Due various satellite orbit parameters and specifications different sensors, storage formats, projections, spatial resolutions, revisit periods these are vastly different. In addition, received continuously by each center arrives at a faster code rate; it is best ingest archive newly ensure users access latest retrieval distribution services. Hence, an...

Urbanization is accelerating at a rapid rate, which has introduced many challenges, especially in the field of urban planning. Under backdrop global urbanization, some cities are particularly vulnerable to climate change and natural disasters that influenced by unplanned expansion. Rational planning functional areas needs be strengthened improve scientific approach urbanization. In this study, classification based on dual-modal data (i.e., remote sensing image user behavior data) was...

The evolution rules of different land covers show some similarity, which poses a huge challenge for high-accuracy land-cover classification. Therefore, the full extraction multiscale timing-dependence features is important mining seasonal and phenological change laws improving accuracy time-series However, traditional methods are often unable to fully detect global local information generated during covers, resulting in incomplete being extracted low classification accuracy. Informer network...

Geochemical data is crucial for reflecting geological features and extensively applied in mineral exploration, environmental impact assessment research. However, the high economic cost of geochemical analysis hinders large-scale studies, leading to low spatial resolution, especially remote areas. Although sensing provides rich surface spectral information shows a strong correlation with features, its accuracy inversion insufficient. Therefore, we improve reliability by fusing multi-source...