Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Urban Ecosystem Services Value: A Case Study of Chengdu, Southwestern China

Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure.
Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.
Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground.
Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications

Digital image correlation (DIC) is a commonly-adopted technique in geoscience and natural hazard studies to measure the surface deformation of various geophysical phenomena. In the last decades, several different correlation functions have been developed. Additionally, some authors have proposed applying DIC to other image representations, such as image gradients or orientation. Many works have shown the reliability of specific methods, but they have been rarely compared. In particular, a formal analysis of the impact of different sources of noise is missing. Using synthetic images, we analysed 15 different combinations of correlation functions and image representations and we investigated their performances with respect to the presence of 13 noise sources. Besides, we evaluated the influence of the size of the correlation template. We conducted the analysis also on terrestrial photographs of the Planpincieux Glacier (Italy) and Sentinel 2B images of the Bodélé Depression (Chad). We observed that frequency-based methods are in general less robust against noise, in particular against blurring and speckling, and they tend to underestimate the displacement value. Zero-mean normalised cross-correlation applied to image intensity showed high-quality results. However, it suffers variations of the shadow pattern. Finally, we developed an original similarity function (DOT) that proved to be quite resistant to every noise source.
UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSEVIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation.
An Improved Symbiosis Particle Swarm Optimization for Solving Economic Load Dispatch Problem

To realize the sustainable development of social economy, energy conservation and emission reduction has become one of the problems that must be considered in the current power system. Under the electric power market system, the economic load dispatch problem not only is important but also has practical significance and broad application prospects. In order to minimize the costs of electric-power generation, the load capacity should be reasonably assigned among many different generating sets. In this paper, an improved symbiosis particle swarm optimization algorithm was proposed, aiming at providing a better solution to this problem. First of all, a mathematical model was established with certain constraints, which successfully converted the practical problem into a mathematical one. Then, to balance the global optimization and local search capability, an improved symbiosis particle swarm optimization algorithm with mutualistic symbiosis strategy in nature was presented. The improved symbiosis particle swarm optimization algorithm consisted of three swarms inspired by the proverb “two heads are better than one,” and its specific analysis was through the standard test functions. At last, the economic load dispatch problem could be optimized by the proposed improved symbiosis particle swarm optimization algorithm. In addition, two different kinds of practical examples were also adopted for algorithm evaluation. From the simulation results, it can be seen clearly that the costs of electric-power generation gained were the lowest compared with the results of particle swarm optimization algorithm, improved chaos particle swarm optimization algorithm, and symbiotic organisms search algorithm, well demonstrating the effectiveness of the improved symbiosis particle swarm optimization algorithm in solving the economic load dispatch problem.
Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis

Leptospirosis, an environmental infectious disease of bacterial origin, is the infectious disease with the highest associated mortality in Seychelles. In small island territories, the occurrence of the disease is spatially heterogeneous and a better understanding of the environmental factors that contribute to the presence of the bacteria would help implement targeted control. The present study aimed at identifying the main environmental parameters correlated with animal reservoirs distribution and Leptospira infection in order to delineate habitats with highest prevalence. We used a previously published dataset produced from a large collection of rodents trapped during the dry and wet seasons in most habitats of Mahé, the main island of Seychelles. A land use/land cover analysis was realized in order to describe the various environments using SPOT-5 images by remote sensing (object-based image analysis). At each sampling site, landscape indices were calculated and combined with other geographical parameters together with rainfall records to be used in a multivariate statistical analysis. Several environmental factors were found to be associated with the carriage of leptospires in Rattus rattus and Rattus norvegicus, namely low elevations, fragmented landscapes, the proximity of urbanized areas, an increased distance from forests and, above all, increased precipitation in the three months preceding trapping. The analysis indicated that Leptospira renal carriage could be predicted using the species identification and a description of landscape fragmentation and rainfall, with infection prevalence being positively correlated with these two environmental variables. This model may help decision makers in implementing policies affecting urban landscapes and/or in balancing conservation efforts when designing pest control strategies that should also aim at reducing human contact with Leptospira-laden rats while limiting their impact on the autochthonous fauna.
PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification

Airborne laser scanning (ALS) point cloud has been widely used in the fields of ground powerline surveying, forest monitoring, urban modeling, and so on because of the great convenience it brings to people’s daily life. However, the sparsity and uneven distribution of point clouds increases the difficulty of setting uniform parameters for semantic classification. The PointNet++ network is an end-to-end learning network for irregular point data and highly robust to small perturbations of input points along with corruption. It eliminates the need to calculate costly handcrafted features and provides a new paradigm for 3D understanding. However, each local region in the output is abstracted by its centroid and local feature that encodes the centroid’s neighborhood. The feature learned on the centroid point may not contain relevant information of itself for random sampling, especially in large-scale neighborhood balls. Moreover, the centroid point’s global-level information in each sample layer is also not marked. Therefore, this study proposed a modified PointNet++ network architecture which concentrates the point-level and global features on the centroid point towards the local features to facilitate classification. The proposed approach also utilizes a modified Focal Loss function to solve the extremely uneven category distribution on ALS point clouds. An elevation- and distance-based interpolation method is also proposed for the objects in ALS point clouds which exhibit discrepancies in elevation distributions. The experiments on the Vaihingen dataset of the International Society for Photogrammetry and Remote Sensing and the GML(B) 3D dataset demonstrate that the proposed method which provides additional contextual information to support classification achieves high accuracy with simple discriminative models and new state-of-the-art performance in power line categories.
A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.
Towards Higher Code Quality in Scientific Computing

Källén, M. 2021. Towards Higher Code Quality in Scientific Computing. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 2000. 52 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-1107-4. In scientific computing and data science, computer programs employing mathematical and statistical models are used for obtaining knowledge in different application domains. The results of these programs form the basis of among other things scientific papers and important desicions that may e.g. affect people's health. Consequently, correctness of the programs is of great importance. To reduce the risk of defects in the source code, and to not waste human resources, it is important that the code is maintainable, i.e. not unnecessarily hard to analyze, test, modify or reuse. For these reasons, this thesis strives towards increased maintainability and correctness in code bases for scientific computing and data science. Object-oriented programming is a programming paradigm that facilitates writing maintainable code, by providing mechanisms for reuse and for division of code into smaller components with restricted access to each others data. Further, it makes extending a code base without changing the existing code possible, increasing flexibility and decreasing the risk of breaking existing functionality. However, in many cases, object-orientation trades its benefits for performance. For some scientific computing programs, performance is essential, e.g. because the results are unusable if they are produced too late. In the first part of this thesis, it is shown that object-oriented programming can be used to improve the quality of an important group of scientific computing programs, with only a small impact on performance. The aim of the second part of the thesis is to contribute to understanding of, and improve quality in, source code for data science. A large corpus of Jupyter notebooks, a tool frequently used by data scientists for writing code, is studied. Results presented suggest that cloned code, i.e. identical or close to identical code that recurs in different places, is common in Jupyter notebooks. Code cloning is important from a perspective of maintenance as well as for research. Additionally, the most frequently called library functions from Python, the language used in the vast majority of the notebooks, are studied. A large number of combinations of parameters for which it is possible to pass values that may lead to unexpected behavior or decreased maintainability are identified. The existence and consequences of occurrences of such combinations of values in the corpus are evaluated. To reduce the risk of future defects in source code calling these functions, improvements are suggested to the developers of the functions. Malin Källén, Department of Information Technology, Division of Scientific Computing, Box 337, Uppsala University, SE-751 05 Uppsala, Sweden. Department of Information Technology, Computational Science, Box 337, Uppsala University, SE-75105 Uppsala, Sweden.
Carbonic anhydrase IX: a regulator of pH and participant in carcinogenesis

Carbonic anhydrase IX (CAIX) is a transmembrane metalloenzyme which is upregulated in tumour cells under hypoxic conditions. CAIX expression is induced by the accumulation of hypoxia-inducible factor-1α and has several downstream effects, including acidification of the extracellular pH, loss of cellular adhesion and increased tumour cell migration. CAIX is upregulated in a variety of solid organ tumours and has prognostic implications. High CAIX protein expression is a marker of poor prognosis in breast, lung, ovarian and bladder carcinomas. Conversely, low expression is an indicator of poor prognosis in clear cell renal cell carcinoma (CCRCC). CAIX immunohistochemistry is useful diagnostically to identify metastatic CCRCC, and the recently recognised clear cell papillary renal cell carcinoma. There is much interest in targeting CAIX with monoclonal antibodies and small molecule inhibitors. There are several small molecule inhibitors under development which have shown promising results in clinical trials. In this paper, we provide an overview of the role of CAIX in tumourigenesis and outline its use as a prognostic, diagnostic and therapeutic biomarker.
Inventory of shipping emissions in Izmit Gulf, Turkey

Ships are significant emissions sources in transportation sector. The environmental effects of shipping emissions become more serious because of insufficient international rules and inspections. Especially in inland waters, canals, straits, gulfs, and port areas emissions effects on environment and health are more important. Izmit Gulf is the major industrial, transport, and inland water region which is affected from shipping emissions with 37 ports and industrial plants. In this study, NOx, SO2, CO2, HC, and PM emission amounts from 11,645 ships called to Izmit Gulf in 2005. These emissions are classified according to ships operation modes and ship types. Annual shipping emissions are estimated as 5,356 t yr−1 for NOx, 4,305 t yr−1 for SO2, 254,261 t yr−1 for CO2, 232 t yr−1 for HC and 487 t yr−1 for PM. To determine the most probably effected regions in the gulf, the spatial distribution of NOx emissions within the Gulf region has been prepared in 1 × 1 Nm2 (Nautical miles) grid cells based on ship movement data along the various routes. Ships in Izmit Gulf contribute to urban pollution with sulfur dioxide significantly. © 2009 American Institute of Chemical Engineers Environ Prog, 2010
Airborne Microorganisms From Livestock Production Systems and Their Relation to Dust

Large amounts of airborne microorganisms are emitted from livestock production. These emitted microorganisms may associate with dust, and are suspected to pose a risk of airborne infection to humans in vicinity and to animals on other farms. However, the extent to which airborne transmission may play a role in the epidemic, and how dust acts as a carrier of microorganisms in the transmission processes is unknown. The authors present the current knowledge of the entire process of airborne transmission of microorganisms—from suspension and transportation until deposition and infection—and their relation to dust. The sampling and the mitigation techniques of airborne microorganisms and dust in livestock production systems are introduced as well.