Large-scale Variations Research Articles

Unraveling the interplay between phylogeny and chemical niches in epiphytic macrolichens

This study aims to elucidate the connection between the phylogeny of epiphytic macrolichens and their chemical niches. We analyzed published floristic and environmental data from 90 canopies of Picea glauca x engelmannii across various forest settings in British Columbia. To explore the concordance between a principal coordinates analysis of the cladistic distance matrix and a global non-metric multidimensional scaling of the ecological distance matrix, we used Procrustean randomization tests. The findings uncover a robust association between large-scale macrolichen phylogeny and canopy throughfall chemistry. The high calcium-scores of the studied species effectively distinguished members of the Peltigerales from those of the Lecanorales, although parameters linked with Ca such as Mn, Mg, K, bark-, and soil-pH, may contribute to the niche partitioning along the oligotrophic—mesotrophic gradient. The substantial large-scale phylogenetic variation in the macrolichens’ Ca-scores is consistent with an ancient adaptation to specialized chemical environments. Conversely, the minor variation in Ca-scores within families and genera likely stems from more recent adaptation. This study highlights crucial functional and chemical differences between members of the Lecanorales and Peltigerales. The deep phylogenetic connection to the chemical environment underscores the value of lichens as transferable bioindicators for the chemical environment and emphasizes the importance of elucidating the intricate interplay between chemical factors and lichen evolution.

Two-dimensional ASEP model to study density profiles in CVD growth

The growth of two-dimensional (2D) transition metal dichalcogenides using chemical vapor deposition has been an area of intense study, primarily due to the scalability requirements for potential device applications. One of the major challenges of such growths is the large-scale thickness variation of the grown film. To investigate the role of different growth parameters computationally, we use a 2D asymmetric simple-exclusion process (ASEP) model with open boundaries as an approximation to the dynamics of deposition on the coarse-grained lattice. The variations in concentration of particles (growth profiles) at the lattice sites in the grown film are studied as functions of parameters like injection and ejection rate of particles from the lattice, time of observation, and the right bias difference between the hopping probabilities towards right and towards left) imposed by the carrier gas. In addition, the deposition rates at a given coarse-grained site is assumed to depend on the occupancy of that site. The effect of the maximum deposition rate, i.e., the deposition rate at a completely unoccupied site on the substrate, has been explored. The growth profiles stretch horizontally when either the evolution time or the right bias is increased. An increased deposition rate leads to a step-like profile, with the higher density region close to the left edge. In 3D, the growth profiles become more uniform with the increase in the height of the precursor with respect to the substrate surface. These results qualitatively agree with the experimental observations.

Multiple scale method integrated physics-informed neural networks for reconstructing transient natural convection

This study employs physics-informed neural networks (PINNs) to reconstruct multiple flow fields in a transient natural convection system solely based on instantaneous temperature data at an arbitrary moment. Transient convection problems present reconstruction challenges due to the temporal variability of fields across different flow phases. In general, large reconstruction errors are observed during the incipient phase, while the quasi-steady phase exhibits relatively smaller errors, reduced by a factor of 2–4. We hypothesize that reconstruction errors vary across different flow phases due to the changing solution space of a PINN, inferred from the temporal gradients of the fields. Furthermore, we find that reconstruction errors tend to accumulate in regions where the spatial gradients are smaller than the order of 10−6, likely due to the vanishing gradient phenomenon. In convection phenomena, field variations often manifest across multiple scales in space. However, PINN-based reconstruction tends to preserve larger-scale variations, while smaller-scale variations become less pronounced due to the vanishing gradient problem. To mitigate the errors associated with vanishing gradients, we introduce a multi-scale approach that determines scaling constants for the PINN inputs and reformulates inputs across multiple scales. This approach improves the maximum and mean errors by 72.2% and 6.4%, respectively. Our research provides insight into the behavior of PINNs when applied to transient convection problems with large solution space and field variations across multiple scales.

An Improved YOLOv8 Network for Multi-Object Detection with Large-Scale Differences in Remote Sensing Images

Aiming to address the challenges of low object detection precision in remote sensing images due to high background complexity and significant target scale variations, a novel model for large-scale disparate object detection in remote sensing images is proposed based on the modified YOLOv8. The model incorporates a Context Aggregation Module (CAM) with an attention mechanism in the backbone network to exploit contextual information, enabling multi-scale feature fusion for effective small object detection. The Neck network utilizes GSConv modules and employs the Slim-Neck design paradigm to enhance model robustness, making it better suited for detecting objects in high-complexity backgrounds. Furthermore, the model adopts the Wise-IoU as the loss function, incorporating a dynamic nonmonotonic focusing mechanism and a gradient gain allocation strategy to enhance the overall performance of disparate object detection. The experimental results indicate that promising performance improvements in the face of large-scale variations in remote sensing image targets. Specifically, the model achieves mAP values of 71.4% and 90.3% on the DOTA and NWPU VHR-10 datasets, respectively, representing increases of 4.4% and 3.7% compared to the original model. Compared to other typical algorithms, it also has considerable advantages in both comprehensive detection accuracy and detection speed.

DADNet: text detection of arbitrary shapes from drone perspective based on boundary adaptation

The rapid development of drone technology has made drones one of the essential tools for acquiring aerial information. The detection and localization of text information through drones greatly enhance their understanding of the environment, enabling tasks of significant importance such as community commercial planning and autonomous navigation in intelligent environments. However, the unique perspective and complex environment during drone photography lead to various challenges in text detection, including diverse text shapes, large-scale variations, and background interference, making traditional methods inadequate. To address this issue, we propose a drone-based text detection method based on boundary adaptation. We first conduct an in-depth analysis of text characteristics from a drone’s perspective. Using ResNet50 as the backbone network, we introduce the proposed Hybrid Text Attention Mechanism into the backbone network to enhance the perception of text regions in the feature extraction module. Additionally, we propose a Spatial Feature Fusion Module to adaptively fuse text features of different scales, thereby enhancing the model’s adaptability. Furthermore, we introduce a text detail transformer by incorporating a local feature extractor into the transformer of the text detail boundary iteration optimization module. This enables the precise optimization and localization of text boundaries by reducing the interference of complex backgrounds, eliminating the need for complex post-processing. Extensive experiments on challenging text detection datasets and drone-based text detection datasets validate the high robustness and state-of-the-art performance of our proposed method, laying a solid foundation for practical applications.

Optical Genome Mapping—A Cytogenomic Tool for Prenatal Diagnostics

Chromosome aberration is the main cause of human inherited diseases. The routine clinical tests still rely on the standardization of cellular genetics. In recent years, Optical Genome Mapping (OGM) has been developed as a high-view method to detect large-scale structural variation of human genome. It is capable of detecting structural variations which are difficult to be detected by other methods. OGM preliminarily applied in the comprehensive identification of genomic structural variations, the visual delineation of chromosomal rearrangement patterns, and the diagnosis of some dynamic mutation diseases. This review primarily focuses on the technical attributes of OGM and its application in the field of prenatal diagnosis.

RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR

Object detection is a fundamental task in computer vision. Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). First, we incorporate cascaded group attention (CGA) into the attention-driven feature interaction module. By capturing features at different levels, it enhances the interaction between features through cascading and improves computational efficiency. Additionally, we propose an enhanced bidirectional feature pyramid network (EBiFPN) to facilitate multi-scale feature fusion. By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. Experimental results on the satellite imagery multi-vehicles dataset (SIMD) and the high-resolution remote sensing object detection (TGRS-HRRSD) dataset show that the improved algorithm achieved mean average precision (mAP) of 78.2% and 91.6% at an intersection over union threshold of 0.5, respectively, which is an improvement of 2.0% and 1.5% over the baseline model. This result demonstrates the effectiveness and robustness of our proposed method for remote sensing image object detection.

Validating a cassava production spatial disaggregation model in sub-Saharan Africa.

Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable.

Ecophenotypic variation in a cosmopolitan reef-building coral suggests reduced deep-sea reef growth under ocean change

Sensitivity of reef-building corals to environmental factors has far-reaching ecosystem implications, especially in the limited number of cold-water coral (CWC) species that form reefs in the deep sea. Understanding CWC responses to large-scale oceanographic variation in their natural habitat can elucidate their sensitivity to global anthropogenic stressors. Here, we use skeletal samples to analyse fine-scale phenotypic variation in the widespread reef-building CWC Desmophyllum pertusum (Lophelia pertusa) in relation to broad physicochemical gradients in different sites across the Atlantic Ocean and Mediterranean Sea. We find evidence, amidst local and regional differentiation, of species-wide growth responses to physicochemical factors, mainly affecting corallite length, width and their ratio (slenderness). Our results suggest that higher temperature and lower oxygen levels negatively affect skeletal linear extension and budding rate of polyps. As also hinted by the reduced corallite length and slenderness in less developed reefs, these widespread responses may lead to a general decline in CWC reef growth rates as a long-term consequence of ocean warming and deoxygenation. Given this relevance, such responses can be used to model reef growth in a changing ocean.

Multi‐scale feature extraction for energy‐efficient object detection in remote sensing images

AbstractObject detection in remote sensing images aims to interpret images to obtain information on the category and location of potential targets, which is of great importance in traffic detection, marine supervision, and space reconnaissance. However, the complex backgrounds and large scale variations in remote sensing images present significant challenges. Traditional methods relied mainly on image filtering or feature descriptor methods to extract features, resulting in underperformance. Deep learning methods, especially one‐stage detectors, for example, the Real‐Time Object Detector (RTMDet) offers advanced solutions with efficient network architectures. Nevertheless, difficulty in feature extraction from complex backgrounds and target localisation in scale variations images limits detection accuracy. In this paper, an improved detector based on RTMDet, called the Multi‐Scale Feature Extraction‐assist RTMDet (MRTMDet), is proposed which address limitations through enhancement feature extraction and fusion networks. At the core of MRTMDet is a new backbone network MobileViT++ and a feature fusion network SFC‐FPN, which enhances the model's ability to capture global and multi‐scale features by carefully designing a hybrid feature processing unit of CNN and a transformer based on vision transformer (ViT) and poly‐scale convolution (PSConv), respectively. The experiment in DIOR‐R demonstrated that MRTMDet achieves competitive performance of 62.2% mAP, balancing precision with a lightweight design.

Atmospheric Trace Gas Oxidizers Contribute to Soil Carbon Fixation Driven by Key Soil Conditions in Terrestrial Ecosystems.

Microbial oxidizers of trace gases such as hydrogen (H2) and carbon monoxide (CO) are widely distributed in soil microbial communities and play a vital role in modulating biogeochemical cycles. However, the contribution of trace gas oxidizers to soil carbon fixation and the driving environmental factors remain unclear, especially on large scales. Here, we utilized biogeochemical and genome-resolved metagenomic profiling, assisted by machine learning analysis, to estimate the contributions of trace gas oxidizers to soil carbon fixation and to predict the key environmental factors driving this process in soils from five distinct ecosystems. The results showed that phylogenetically and physiologically diverse H2 and CO oxidizers and chemosynthetic carbon-fixing microbes are present in the soil in different terrestrial ecosystems. The large-scale variations in soil carbon fixation were highly positively correlated with both the abundance and the activity of H2 and CO oxidizers (p < 0.05-0.001). Furthermore, soil pH and moisture-induced shifts in the abundance of H2 and CO oxidizers partially explained the variation in soil carbon fixation (55%). The contributions of trace gas oxidizers to soil carbon fixation in the different terrestrial ecosystems were estimated to range from 1.1% to 35.0%. The estimated rate of trace gas carbon fixation varied from 0.04 to 1.56 mg kg-1 d-1. These findings reveal that atmospheric trace gas oxidizers may contribute to soil carbon fixation driven by key soil environmental factors, highlighting the non-negligible contribution of these microbes to terrestrial carbon cycling.

High-quality genome of black wolfberry (Lycium ruthenicum Murr.) provides insights into the genetics of anthocyanin biosynthesis regulation

Abstract Black wolfberry (Lycium ruthenicum Murr.) is an important plant for ecological preservation. In addition, its fruits are rich in anthocyanins and have important edible and medicinal value. However, a high quality chromosome-level genome for this species is not yet available, and the regulatory mechanisms involved in the biosynthesis of anthocyanins are unclear. In this study, haploid material was used to assemble a high-quality chromosome-level reference genome of Lycium ruthenicum, resulting in a genome size of 2,272 Mb with contig N50 of 92.64 Mb, and 38,993 annotated gene models. In addition, the evolution of this genome and large-scale variations compared with the Ningxia wolfberry Lycium barbarum were determined. Importantly, homology annotation identified 86 genes involved in the regulatory pathway of anthocyanin biosynthesis, five of which [LrCHS1 (evm.TU.Chr05.295), LrCHS2 (evm.TU.Chr09.488), LrAOMT (evm.TU.Chr09.809), LrF3'5'H (evm.TU.Chr06.177) and LrAN2.1 (evm.TU.Chr05.2618)] were screened by differential expression analysis and correlation analysis using a combination of transcriptome and metabolome testing. Overexpression of these genes could significantly up- or downregulate anthocyanin-related metabolites. These results will help accelerate the functional genomic research of L. ruthenicum, and the elucidation of the genes involved in anthocyanin synthesis will be beneficial for breeding new varieties and further exploring its ecological conservation potential.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.

The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model. The model can automatically identify polyp regions in gastrointestinal images and accurately label them. In terms of design, the model integrates multi-channel information to enhance the ability and robustness of channel feature expression, thus better coping with the complexity of polyp structures. At the same time, a hierarchical structure is constructed in the model to enhance the model's adaptability to multi-scale targets, effectively addressing the problem of large-scale variations in polyps. Furthermore, a channel attention mechanism is designed in the model to improve the accuracy of target positioning and reduce uncertainty in diagnosis. By integrating these strategies, the proposed gastrointestinal polyp object detection model can achieve accurate polyp detection, providing clinicians with reliable and valuable references. Experimental results show that the model exhibits superior performance in gastrointestinal polyp detection, which helps improve the diagnostic level of digestive system diseases and provides useful references for related research fields.

SAKit: An all-in-one analysis pipeline for identifying novel proteins resulting from variant events at both large and small scales.

Background: Genetic mutations that cause the inactivation or aberrant activation of essential proteins may trigger alterations or even dysfunctions in cellular signaling pathways, culminating in the development of precancerous lesions and cancer. Mutations and such dysfunctions can result in the generation of "novel proteins" that are not part of the conventional human proteome. Identification of these proteins carries a profound potential for unraveling promising drug targets and designing innovative therapeutic models. Despite the emergence of diverse tools for detecting DNA or RNA variants, facilitated by the widespread adoption of nucleotide sequencing technology, these methods primarily target point mutations and exhibit suboptimal performance in detecting large-scale and combinatorial mutations. Additionally, the outcomes of these tools are confined to the genome and transcriptome levels, and do not provide the corresponding protein information resulting from genetic alterations. Results: We present the development of Sequencing Analysis Kit (SAKit), a bioinformatics pipeline for hybrid sequencing analysis integrating long-read and short-read RNA sequencing data. Long reads are utilized for detecting large-scale variations such as gene fusions, exon skipping, intron retention, and aberrant expression in non-coding regions, owing to their excellent coverage capabilities. Short reads serve to validate these findings at breakpoints and splice junctions. Conversely, short reads are employed for identifying small-scale variations, including single nucleotide variants, deletions, and insertions, due to their superior sequencing depth, with long reads providing additional validation. SAKit is designed to perform analyses using inter-species configuration files comprising genome references and annotation data, making it applicable to both human and mouse studies. Furthermore, SAKit implements a hierarchical filtering approach to eliminate low-confidence variants and employs open reading frame (ORF) analysis to translate identified variants into protein sequences. Conclusion: SAKit is a robust and versatile bioinformatics tool designed for the comprehensive identification of both large-scale and small-scale variants from RNA-seq data, facilitating the discovery of novel proteins. This pipeline integrates analysis of long-read and short-read sequencing data, offering a powerful solution for researchers in genomics and transcriptomics. SAKit is freely accessible and open-source, available through GitHub (https://github.com/therarna/SAKit) and as a Docker image https://hub.docker.com/repository/docker/therarna). Implemented primarily within a Snakemake framework using Python, SAKit ensures reproducibility, scalability, and ease of use for the scientific community.

ALMA-IMF

The stellar initial mass function (IMF) is critical to our understanding of star formation and the effects of young stars on their environment. On large scales, it enables us to use tracers such as UV or Hα emission to estimate the star formation rate of a system and interpret unresolved star clusters across the Universe. So far, there is little firm evidence of large-scale variations of the IMF, which is thus generally considered “universal”. Stars form from cores, and it is now possible to estimate core masses and compare the core mass function (CMF) with the IMF, which it presumably produces. The goal of the ALMA-IMF large programme is to measure the core mass function at high linear resolution (2700 au) in 15 typical Milky Way protoclusters spanning a mass range of 2.5 × 103 to 32.7 × 103 M⊙. In this work, we used two different core extraction algorithms to extract ≈680 gravitationally bound cores from these 15 protoclusters. We adopted a per core temperature using the temperature estimate from the point-process mapping Bayesian method (PPMAP). A power-law fit to the CMF of the sub-sample of cores above the 1.64 M⊙ completeness limit (330 cores) through the maximum likelihood estimate technique yields a slope of 1.97 ± 0.06, which is significantly flatter than the 2.35 Salpeter slope. Assuming a self-similar mapping between the CMF and the IMF, this result implies that these 15 high-mass protoclusters will generate atypical IMFs. This sample currently is the largest sample that was produced and analysed self-consistently, derived at matched physical resolution, with per core temperature estimates, and cores as massive as 150 M⊙. We provide both the raw source extraction catalogues and the catalogues listing the source size, temperature, mass, spectral indices, and so on in the 15 protoclusters.

A unified feature-motion consistency framework for robust image matching

Establishing reliable feature matches between a pair of images in various scenarios is a long-standing open problem in photogrammetry. Attention-based detector-free matching with coarse-to-fine architecture has been a typical pipeline to build matches, but the cross-attention module with global receptive field may compromise the structural local consistency by introducing irrelevant regions (outliers). Motion field can maintain structural local consistency under the assumption that matches for adjacent features should be spatially proximate. However, motion field can only estimate local displacements between consecutive images and struggle with long-range displacements estimation in large-scale variation scenarios without spatial correlation priors. Moreover, large-scale variations may also disrupt the geometric consistency with the application of mutual nearest neighbor criterion in patch-level matching, making it difficult to recover accurate matches. In this paper, we propose a unified feature-motion consistency framework for robust image matching (MOMA), to maintain structural consistency at both global and local granularity in scale-discrepancy scenarios. MOMA devises a motion consistency-guided dependency range strategy (MDR) in cross attention, aggregating highly relevant regions within the motion consensus-restricted neighborhood to favor true matchable regions. Meanwhile, a unified framework with hierarchical attention structure is established to couple local motion field with global feature correspondence. The motion field provides local consistency constraints in feature aggregation, while feature correspondence provides spatial context prior to improve motion field estimation. To alleviate geometric inconsistency caused by hard nearest neighbor criterion, we propose an adaptive neighbor search (soft) strategy to address scale discrepancy. Extensive experiments on three datasets demonstrate that our method outperforms solid baselines, with AUC improvements of 4.73/4.02/3.34 in two-view pose estimation task at thresholds of 5°/10°/20° on Megadepth test, and 5.94% increase of accuracy at threshold of 1px in homography task on HPatches datasets. Furthermore, in the downstream tasks such as 3D mapping, the 3D models reconstructed using our method on the self-collected SYSU UAV datasets exhibit significant improvement in structural completeness and detail richness, manifesting its high applicability in wide downstream tasks. The code is publicly available at https://github.com/BunnyanChou/MOMA.

Structure of Reef Fish Families (Butterflyfishes and Angelfishes) at Isolated Oceanic Reefs in the Indian Ocean: Christmas Island and the Cocos (Keeling) Islands

There has been substantial research on the factors that structure reef fish assemblages, but this has mostly focused on nearshore or continental reefs. This study examines patterns of abundance and species composition for two iconic groups of coral reef fishes, angelfishes (family Pomacanthidae) and butterflyfishes (family Chaetodontidae) at two isolated, oceanic reefs in the Indian Ocean: Christmas Island and the Cocos (Keeling) Islands. Six explanatory variables were investigated to determine whether large-scale physical factors are more important than fine-scale biotic factors in structuring reef fish communities on oceanic islands. For angelfishes, depth was the factor that most explained patterns in abundance (explaining 46.5% of the variation), species richness (44.8%) and composition (15.3%), with both abundance and species richness being greater at 20 m than at 5 m. Differences in species composition were greater between depths than between islands. For butterflyfishes, variation in abundance and species richness was best explained by the difference in aspect or exposure among sites, though abundance and composition also differed significantly between Christmas Island and the Cocos (Keeling) Islands. Large-scale variation in the structure of these reef fish assemblages could not be explained based on differences in habitat diversity, or coral and algal cover. This study indicates that large-scale physical factors (island location, exposure, depth) are more important than fine-scale biotic factors in structuring reef fish assemblages on oceanic islands.

MFLD: lightweight object detection with multi-receptive field and long-range dependency in remote sensing images

PurposeBased on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.Design/methodology/approachTo alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.FindingsTo concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.Originality/valueThis paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

The influence of topography on subaqueous gravity flows: a case study from the Jurassic Los Molles Formation, Neuquén Basin, Argentina

ABSTRACT Subaqueous sediment gravity flows experience modifications when they interact with slope and basin-floor topography, impacting facies, geometries, and architectural patterns of the deposits. Understanding these processes is critical for reservoir-quality and trap predictions in deep-water stratigraphic plays. A comprehensive literature summary of the topographic influence on subaqueous gravity flows and an interpretation applied to the basin-floor turbidite system of the late synrift Los Molles Formation in the Neuquén Basin of Argentina are provided. In the study area, a high-resolution satellite image, drone imagery, and 30 logs (about 5000 m total thickness) were measured, focusing on facies analysis, paleocurrents, bed thicknesses, and large-scale thickness variations. Studies describing topographic confinement of sediment gravity flows have used five approaches: 1) Paleocurrent analysis: paleocurrents following structural trends and variations within the same bed (flow deflection and reflection). 2) Facies analysis: complex facies variations on short distances and increased number of hybrid beds and debrites near barriers (flow transformation), loading, convoluted structures, and bidirectional ripples (flow reflection), and sand–mud couplets or thick mudcaps (flow ponding). 3) Small-scale (decimeter to meter) thickness variations: beds thickening towards topography, beds thinning on top of topography (onlaps), and low thinning rates (loss of flow competence, flow containment). 4) Architectural analysis: aggradational stacking, lateral stacking away from topographic barriers, and fill–spill successions. 5) Large-scale thickness (tens of meters) variations: increased fan system thickness across topographic lows. An extended version of an existing classification scheme on degree of confinement is proposed: C0 (unconfined): no flow modification evidence present, and compensational stacking is a classic architectural pattern. C1 (weakly confined): variations in regional paleocurrent directions, minor thickness variations against small-relief barriers, large (basin scale) thickness variations. C2a (confined) to C2b (highly confined): onlaps against high-relief barriers, paleocurrent direction variations on the same bed, bed thickening against topography, facies evidencing flow reflection, increased number of hybrid beds, aggradational stacking, and high percentage of beds continuous over 500 m distances (tabular beds). C2a and C2b are considered end members of a spectrum where the amount of evidence listed can vary. C3 (ponded): sand mud couplets or thick mudcaps. Higher confinement categories might have indicators from the weaker confinement categories. The lower fan in the Los Molles Fm. turbidite system is classified as weakly confined. A complex sediment routing followed structural trends inherited from the rift phase of the basin. The basin-floor had irregular water depths that led to preferred sites of deposition. Hybrid beds are mostly found at the fringes of the lower part of the succession and might be associated with an above-grade slope profile on a footwall scarp. Lastly, the Los Molles Fm. basin-floor beds present high thinning rates (average 1.5 m/km) and the system stacks compensationally, pointing to null interaction against basin margins.