Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations

As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.
Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models

Over the last several decades, thanks to improvements in and the diversification of open-access satellite imagery, land cover mapping techniques have evolved significantly. Notable changes in these techniques involve the automation of different steps, yielding promising results in terms of accuracy, class detection and efficiency. The most successful methodologies that have arisen rely on the use of multi-temporal data. Several different approaches have proven successful. In this study, one of the most recently developed methodologies is tested in the region of Galicia (in Northwestern Spain), with the aim of filling gaps in the mapping needs of the Galician forestry sector. The methodology mainly consists of performing a supervised classification of individual images from a selected time series and then combining them through aggregation using decision criteria. Several of the steps of the methodology can be addressed in multiple ways: pixel resolution selection, classification model building and aggregation methods. The effectiveness of these three tasks as well as some others are tested and evaluated and the most accurate and efficient parameters for the case study area are highlighted. The final land cover map that is obtained for Galicia has high accuracy metrics (an overall accuracy of 91.6%), which is in line with previous studies that have followed this methodology in other regions. This study has led to the development of an efficient open-access solution to support the mapping needs of the forestry sector.
Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.
SAR Oil Spill Detection System through Random Forest Classifiers

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.
Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R2 = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy.
Energy Efficient Resource Allocation and Trajectory Design for Multi-UAV-Enabled Wireless Networks

Unmanned aerial vehicles (UAVs) have been a provision for future wireless networks. However, limited backhaul capacity and power are bottlenecks for deployment and control of UAVs. To tackle these challenges, we propose a cache-enabled UAV networks to store popular files proactively to serve ground users and alleviate the backhaul burden. Taking account of the limited battery capacity, we propose an energy-efficient resource allocation and trajectory design algorithm to maximize the minimum achievable throughput among users. The formulated problem is a non-convex and mixed-integer optimization problem. To facilitate dealing with it, we decouple it into three subproblems and alternately solve them by jointly optimizing cache placement, transmit power, bandwidth allocation, and trajectory using successive convex approximation and block coordinate decent. The algorithm is proved to converge after finite steps of iterations. Numerical results reveal that our algorithm outperforms several baselines in terms of achievable throughput.
Analysis of Groundwater Depletion/Inflation and Freeze-Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.
Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.
An Edge Federated MARL Approach for Timeliness Maintenance in MEC Collaboration

Mobile edge computing (MEC) has been widely studied to provide new schemes for communication-computing systems such as industrial Internet of Things (IoTs), vehicular networks, smart city applications, etc. In this work, we mainly investigate on the timeliness maintenance of the MEC systems where the freshness of the data and computation tasks plays a significant role. We firstly formulate the average age of information (AoI) minimization problem of the UAV-assisted MEC systems. To maintain the system timeliness, we propose a novel multi-agent reinforcement learning (MARL) approach, called edge federated multi-agent actor-critic (MAAC), for joint trajectory planning, data scheduling and resource management in the investigated MEC systems. Through the proposed online learning method, edge devices and center controller learn the interactive policies through local observations and carry out the model-wise communication. We build up a simulation platform for time sensitive MEC systems as a gym environment module and implement the proposed algorithm. Furthermore, the comparisons with a popular MARL solution, MADDPG, show that the proposed approach achieves better performance in terms of data freshness and system stability.
Validation of GPM Rainfall and Drop Size Distribution Products through Disdrometers in Italy

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).
Spatial and Temporal Variability of Minimum Brightness Temperature at the 6.925 GHz Band of AMSR2 for the Arctic and Antarctic Oceans

The minimum brightness temperature (mBT) of seawater in the polar region is an important parameter in algorithms for determining sea ice concentration or snow depth. To estimate the mBT of seawater at 6.925 GHz for the Arctic and Antarctic Oceans and to find their physical characteristics, we collected brightness temperature and sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for eight years from 2012 to 2020. The estimated mBT shows constant annual values, but we found a significant difference in the seasonal variability between the Arctic and Antarctic Oceans. We calculated the mBT with the radiative transfer model parameterized by sea surface temperature (SST), sea surface wind speed (SSW), and integrated water vapor (IWV) and compared them with our observations. The estimated mBT represents the modeled mBT emitted from seawater under conditions of 2–5 m/s SSW and SST below 0 °C, except in the Arctic summer. The exceptional summer mBT in the Arctic Ocean was related to unusually high SST. We found evidence of Arctic amplification in the seasonal variability of Arctic mBT.
Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.
Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.
New Constraints on Slip Behavior of the Jianshui Strike-Slip Fault from Faulted Stream Channel Risers and Airborne Lidar Data, SE Tibetan Plateau, China

The temporal slip behavior of a fault from displaced landforms when there are no chronological data remains poorly understood. The southern segment of the Xiaojiang fault zone (XJFZ) plays an important role in accommodating the lateral extrusion of the SE Tibetan plateau. However, there are few reports on the evolution of the offset landforms and slip behavior of the fault due to the dense vegetation in the region. Here, offset landforms along the Jianshui fault (JSF) in the southern segment of the XJFZ are systematically interpreted and measured using high-resolution satellite imagery, field investigations, and airborne lidar. The risers on the right banks of three stream channels feature similar left-lateral offset characteristics near the town of Dongshanzhai. The left-lateral offsets consist of multiple inflections produced by seismic events, and the offset of each event is similar. These inflections are distributed downstream in a stair-stepped manner. The newly formed inflections are located close to the fault, and the earlier formed ones are eroded by flowing water and migrate downstream. The difference between the amount of downstream erosion of two adjacent inflections varies. Assuming the stream’s long-term erosion rate remains steady, the estimated time intervals between seismic events are different. Combined with the cumulative offset probability density calculation for 92 offsets, the JSF is considered to show a nonperiodic characteristic earthquake recurrence pattern. We also propose a multistage offset evolution model of the stream channel riser. This provides a new way to analyze the seismic recurrence pattern of the fault over a relative time scale.
IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.