Search Results (129)

Results are reported for a series of small-scale experiments that examine the dispersion of dense gas released upstream of an isolated building. The experiments replicate the geometry of the Thorney Island Phase II field tests and show good qualitative agreement with the flow regimes observed therein. The experiments were run in a water flume, and the flow is characterized by the Richardson number ($Ri$), where high $Ri$ represent relatively high density releases. For low $Ri$ the dense cloud flows over and around the building and any fluid drawn into the building wake is rapidly flushed. However, for high Ri, the dense cloud collapses, flows around the building, and is drawn into the wake. The dense fluid layer becomes trapped in the wake and is flushed by small parcels of fluid being peeled off the top of the layer and driven up and out of the wake. Results are presented for the concentration field along the center plane (parallel to the flow) of the building wake and time series of concentration just above the floor and downstream of the building. The time series for low-$Ri$ and high-$Ri$ flows are starkly different, with differences explained in terms of the observed flow regimes. Full article

Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions. Full article

The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application. Full article

Low-impact development (LID) facilities serve as a fundamental approach in urban stormwater management. However, significant variations in land use among different plots lead to discrepancies in runoff reduction demands, frequently leading to either the over- or under-implementation of LID infrastructure. To address this issue, we propose a cost-effective optimization framework grounded in the concept of “Capacity Trading (CT)”. The study area was partitioned into multi-scale grids (CT-100, CT-200, CT-500, and CT-1000) to systematically investigate runoff redistribution across heterogeneous land parcels. Integrated with the Sequential Least Squares Programming (SLSQP) optimization algorithm, LID facilities are allocated according to demand under two independent constraint conditions: runoff coefficient ($\phi$ ≤ 0.49) and runoff control rate ($\eta$ ≥ 70%). A quantitative analysis was conducted to evaluate the construction cost and reduction effectiveness across different trading scales. The key findings include the following: (1) At a constant return period, increasing the trading scale significantly reduces the demand for LID facility construction. Expanding trading scales from CT-100 to CT-1000 reduces LID area requirements by 28.33–142.86 ha under the $\phi$-constraint and 25.5–197.19 ha under the $\eta$-constraint. (2) Systematic evaluations revealed that CT-500 optimized cost-effectiveness by balancing infrastructure investments and hydrological performance. This scale allows for coordinated construction, avoiding the high costs associated with small-scale trading (CT-100 and CT-200) while mitigating the diminishing returns observed in large-scale trading (CT-1000). This study provides a refined and efficient solution for urban stormwater management, overcoming the limitations of traditional approaches and demonstrating significant practical value. Full article

Semantic segmentation has emerged as a critical research area in Earth observation. This paper proposes a novel end-to-end semantic segmentation network, the Nested Cross-Scale and Bidirectional Feature Fusion Network (NCSBFF-Net), to address issues such as intra-class heterogeneity, inter-class homogeneity, scale variability, and the classification of tiny objects. Specifically, a CNN-based lightweight feature pyramid module is employed to extract contextual information across multiple scales, thereby addressing intra-class heterogeneity. The NCSBFF module leverages features from both shallow and deep layers and is designed to fuse multi-scale features, thereby enhancing inter-class semantic differences. Additionally, the shallowest feature is passed to the Shuffle Attention block in the NCSBFF module, which adaptively filters out weak details and highlights critical information for the classification of tiny objects. Extensive experiments were conducted on the Potsdam and Vaihingen benchmarks. Experiment results demonstrate that the NCSBFF-Net outperforms state-of-the-art methods, achieving a better trade-off between accuracy and efficiency, with a 5% improvement in mIoU significantly enhancing the recognition capability of small and complex objects, such as vehicles and irregular land parcels, in challenging scenes, and a 1.73% increase in accuracy demonstrating a better balance between computational efficiency and segmentation accuracy, providing an optimized solution for deployment on edge devices. Full article

The decrease in grain plantation areas poses a growing concern for global food security. China, with its large population, increasingly diversified food demands, and relatively small cultivated lands, has suffered deeply from this phenomenon (non-grain production, NGP) in recent years. Since 2020, the central government of China has claimed to deal with this problem by attracting agriculturalists and organizations involved in grain plantation. In this context, understanding the global NGP of the national situation is vital for policy making. Remote sensing is regarded as the most effective and accurate method for this purpose, but existing studies have mainly focused on algorithms operating at the local scale or exploring grain-producing capability from the perspective of agricultural space. As such, the characterization of NGP on a national scale remains deficient. In this study, we tried to bridge the gap through spatio-analysis with a newly published nationwide crop pattern and land use geo-datasets; the quantitative, spatial, and structural features, as well as the utilization of NGP cropland in the year 2019, were observed. The results showed that about 60% of the cropland was used for non-grain plantation. About 15% of the NGP parcels were cultivated with grains at least three times in the past 4 years, and of these 60% and 40% were parcels with double- or single-season plantation, respectively, which could result in a 16–22% increase in the grain-sown area compared with 2019. Forest and grassland were the dominant non-cropping categories which NGP cropland transferred into, indicating more time and economic cost for regaining grains. NGP parcels also presented spatio-heterogeneity regarding cropping intensity and transformation. Parcels with double-season plantation mostly emerged in northern, central, and southern provinces, while those with single-season plantation were always located in northeastern and western provinces. The parcels that were transferred into forest or grassland mainly appeared in southern and Inner Mongolia, respectively, while the parcels in northern and central areas mostly continued cropping. According to these results, we propose remediation policies focusing on raising the cropping intensity of cultivated land in central and northern provinces due to their advantages of water, heat, terrain, and land use change features. Future work is warranted based on this study’s deficiencies and uncertainties. As a forerunner, this study provides a holistic observation of the NGP phenomenon in mainland China on a national scale, and the findings can inform improvements in land use policies concerning grain production and food security in China. Full article

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km², with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world. Full article

The present work unveils the role of small farming plots (less than 5 ha) in the context of food security. It determines their contribution by estimating the spatial distribution (location), the crop types (diversity), the crop area extent (acreage), and the yield (production), factors that remain unclear, mainly because the official statistical offices rarely include them in surveys. The development introduces a novel RS-based approach that fulfills this gap. It provides stakeholders with the appropriate tools to accurately and timely acquire crop type map information and objectively quantify their crop production capabilities. Approaches based on the Land Parcel Identification System (LPIS) of the Integrated Administration and Control System (IACS) applied by many countries in Europe are proved useful in providing information on location, diversity, and acreage but not crop production per farm owner applying and eligible for receiving subsidies. The developed RS approach is implemented in twenty European NUTS-3 regions and one in Africa. Nevertheless, in this research, we focus on its development, testing, and evaluation in three pilot prefectures of Greece, producing the corresponding land cover maps. Notably, the unbiased crop area computation and the crop production estimates are performed only for the highly accurate key crop products (per crop type classification, FScore > 75%), considering that the key crop production estimations are obtained by combining the key crop areas with the field-level yields provided by the key informant surveys. The above choice ensures that the estimation of crop production will be derived only for the best-classified crops per reference region. The RS approach reduces the error propagation when estimating the area and production of the crop types that are classified with low or very low accuracy levels. These levels could reduce the strength of the overall conclusions about the main contributions of small farming plots. Potential changes occurring in the key crop cultivations of small farming plots are also estimated and mapped using the LPIS geodatabase. Under various environmental and territorial conditions, the results of the RS approach show good classification accuracies for several key crops per reference region. Their integration with the existing official statistical data and those derived from the LPIS geodatabase shows the consistency and significant contribution in estimating all the factors needed to determine the small farming plots. Finally, the applied innovative integrated approach can be expanded beyond the Greek case to cover other regions with various agricultural practices. Full article

The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction. Full article

Investigating the sonosphere can serve as a valuable proxy for understanding various ecosystem processes. Consequently, an ecoacoustic perspective broadens our capacity to understand how airborne sounds interact along an ecotone at the soil surface with the subterranean sounds generated within a pedon. We explored techniques that could detect, quantify, and analyze the sonic dimensions of a sonosphere in the form of sounds within a unit of soil (sonopedon), sounds from a landscape unit (sonotope), and the sonic ecotone (sonotone) where these phenomena converge. We recorded sounds for 24 h over 20 days in September 2024 at 40 sites distributed evenly across a small rural parcel of agricultural land in Northern Italy. We utilized a sound recording device fabricated with a sonic probe that simultaneously operated inside the soil and the grounds’ surface, which successfully captured sounds attributable both to the soilscape and to the landscape. We calculated the Sonic Heterogeneity Indices, SHI_tf and SHI_ft, and analyzed the Spectral and Temporal Sonic Signatures along with Spectral Sonic Variability, Effective Number of Frequency Bins, and Sonic Dissimilarity. Each calculation contributed to a detailed description of how the sonosphere is characterized across the frequency spectrum, temporal dynamics, and sound sources. The sonosphere in our study area, primarily characterized by the low-frequency spectra, possessed a mix of biological, geophysical, and anthropogenic sounds displaying distinct temporal patterns (sonophases) that coincided with astronomic divisions of the day (daytime, twilights, and nighttime). Full article

Producing biochar from residual biomass is an opportunity for health, environmental, and economic benefits to farmers in small traditional parcels, which are widespread in Latin America. This study presents a life cycle assessment of biochar in two circular economy scenarios: soil amendment and wastewater filtration. Seven mid-point environmental impact categories were assessed using the CML-IA method: acidification (AP), abiotic depletion (ADP), fossil fuels depletion (ADP-FF), eutrophication (EP), global warming (GWP), human toxicity (HTP), and smog formation (POCP). The soil amendment scenario showed lower impacts per tonne of biochar in all categories, especially for GWP (−801.3 kg CO₂eq) and ADP-FF (−374.3 MJ), compared to the filtration scenario (−123.54 kg CO₂eq and 827.85 MJ). Negative GWP values reflect reduced emissions from avoided fertilizers and carbon sequestration. However, POCP and HTP increased due to air emissions (CH₄, NOx, NMVOC, and PM₁₀) from the kiln. In both scenarios, biochar production contributed to 40–90% of the total impacts. Indirect emissions from electricity used for water pumping were identified as a hotspot in the filtration scenario. Full article

Onsite wastewater treatment systems (OWTSs or septic systems), when properly sited, designed, operated, and maintained, treat domestic wastewater to reduce impacts on and maintain sustainability of aquatic resources. However, when OWTSs are not performing as expected, they can be a potential source of human fecal pollution to recreational waters, resulting in an increased risk of illness to swimmers. Quantifying the contribution of poor-performing OWTSs relative to other sources of fecal pollution is particularly challenging in wet weather when various sources commingle as they flow downstream. This study aimed to estimate the total load of human fecal pollution from OWTSs in an arid watershed with municipal separate storm sewer systems (MS4). The novel study design sampled HF183, a DNA-based human marker, from six small catchments containing only OWTSs and no other known human fecal sources, such as sanitary sewer collection systems or people experiencing homelessness. Then, the human fecal loading from the representative catchments was extrapolated to the portions of the watershed that were not sampled but contained OWTSs. Flow-weighted mean HF183 concentrations ranged from 10⁴ to 10⁷ gene copies/100 mL across 29 site-events. HF183 mass loading estimates were normalized to the number of parcels per catchment and inches of rainfall per storm event. Assuming the normalized loading estimate was representative, extrapolation to all of the OWTS parcels in the watershed and average annual rainfall quantity illustrated that HF183 loading from OWTSs was a small but measurable fraction of the total HF183 mass loading emanating at the bottom of the watershed. Clearly, other human fecal sources contributed HF183 during storm events in this watershed. The loading estimate approach used in this study could be applied to other watersheds facing similar challenges in prioritizing resources for monitoring and mitigation among co-located human fecal sources. Full article

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an $F_1$-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. Full article

Parcel transportation is a task that is expected to be highly automated with the development of application-specific mobile robots. This paper presents the design and implementation of a parcel transportation and delivery mechanism aimed at converting a general-purpose indoor omnidirectional robot into an indoor delivery robot. The design of this new mechanism has considered the best placement in the robot and the limitation of not exceeding the original robot diameter. The mechanism consists of a basket with a lid that allows for the manual loading and automatic unloading of parcels. Despite the space limitations imposed by the general-purpose robot design, the designed mechanism can transport up to 90% of the packages received in an educational building. The mechanism was empirically validated by conducting 125 static manual loading experiments, 150 static unloading experiments, and 50 complete parcel delivery experiments. Results show that the delivery robot can efficiently deliver 78% of the total packages received in the building: envelopes, very small parcels, and small parcels. In the case of medium parcels, the delivery was unsuccessful in 30% of cases, in which the parcel did not properly slide out of the basket. Full article

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments. Full article