The state of the land surface and the water cycle over the South and East Asia can be determined by space observation. New or significantly improved algorithms have been developed and evaluated against ground measurements. Variables retrieved include land surface properties, i.e. NDVI, LAI, FPAR, albedo, soil moisture, glacier and lake levels. Based on these biophysical parameters derived from microwave and optical remote sensing observations, a hybrid remotely sensed evapotranspiration (ET) estimation model named ETMonitor was developed and applied to estimate the daily actual ET of the Southeast Asia at a spatial resolution of 1 km. The changes in glaciers and lakes on the Tibetan Plateau, and the drainage links between glaciers and lakes are determined in this climate-sensitive region.@en

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet's performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m × 5m and 10m × 10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors. @en

Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds' irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling. @en

China has achieved significant progress on wastewater treatment and aquatic environmental protection. However, leakage (in- and exfiltration) of sewer systems is still an issue. By using the statistical data of water and wastewater in 2016 in China, and the person loads (PLs) of water and wastewater in Singapore, the leakage fractions of hydraulic flow, organic carbon (COD), nitrogen (N) and phosphorus (P) mass loading, and in-sewer COD biological removal in the sewer systems of China (except Hong Kong, Macau and Taiwan), Shanghai, Guangzhou and Beijing were reported for the first time. The fractions of hydraulic flow infiltration (13%, Shanghai and Guangzhou) and exfiltration (39%, China) were calculated. Except Beijing, whose sewer networks are under appropriate management with small leakage fractions, the exfiltration fractions of COD (including in-sewer biological COD removal) ranged from 41% (Shanghai) to 66% (China) and averaged 55%; N ranged from 18% (Shanghai) to 48% (China) and averaged 33%; and P ranged from 23% (Shanghai and Guangzhou) to 44% (China) and averaged 30%. The exfiltrated sewage, COD, N and P not only wastes resources, but also contaminates the aquatic environment (especially groundwater) and contributes to 'black and odorous water bodies'. In- and exfiltration in the sewer network leads to low influent COD concentration, C/N ratio and high inorganic solids and inert particulate COD concentrations of many municipal wastewater treatment plants (WWTPs) causing high cost for nutrient removal, poor resource recovery, additional reactor/settler volume requirement and other operational problems. Therefore, tackling sewer leakage is of primary importance to today's environment in China. Recommendations for the inspection of sewer systems and the rehabilitation of damaged sewers as well as the development of design and operation guidelines of municipal WWTPs tailored to the specific local sewage characteristics and other conditions are proposed.@en

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.@en

In a mechanistic-empirical (ME) pavement design method, the modulus of asphalt concrete (AC) is essential. The in-situ AC modulus can be obtained by the surface wave method (SWM). However, the measured modulus needs to be adjusted to the design frequency values. This study aimed to propose a frequency adjustment method for the in-situ AC seismic modulus. For this purpose, coring and dynamic modulus tests, FWD (Falling Weight Deflectometer) tests, and PSPA (Portable Seismic Pavement Analyzer) tests were carried out in the accelerated pavement test (APT). The master curves generated by the dynamic modulus results correlated well with the moduli determined from the other tests. Based on this relationship, a frequency adjustment factor was developed for the seismic modulus of the undamaged AC layer. For the damaged AC layer, the proposed factor was modified by incorporating the effect of the damage. Finally, to validate the frequency adjustment factor, the measured maximum tensile strains in the APT were compared with the values determined from the adjusted seismic moduli. The results prove that the frequency adjustment factor is appropriate to obtain the design modulus from the surface wave test. The PSPA test is also recommended to obtain the in-situ AC seismic modulus.@en

A decade ago, membrane bioreactors (MBR) were considered as a potential technology for replacing conventional activated sludge (CAS) processes. Indeed, MBR experienced a period of rapid development around 2009, especially in China. In recent years, however, there has been a sharp drop in the number of MBR installations all around the world - with the exception of in China. It is important to understand the reasons for this difference between the reactions of China and other countries. High-quality effluent and a smaller MBR footprint are also associated with high energy consumption and operational costs. Therefore, it is necessary to carefully evaluate the advantages and disadvantages of MBR. In this study, a model (including capital and operational costs, operational efficiency and stability, resources saving and recovery and an evaluation of the sustainability index (SI) of MBR over CAS) was established to comprehensively evaluate the effects of MBR on economy, technology and management. The model calculations of SI demonstrate SI > 1.0. This means, briefly put, that MBR is not a sustainable process when compared to CAS, which is extensively applied but generally thought to be an unsustainable process. The sensitivity analysis of both related factors and weighted coefficients in the model reveals that there is almost no possibility of attaining SI < 1.0 (more sustainable than CAS). For this reason, it is not recommended that MBRs extensively replace CAS when upgrading and constructing wastewater treatment plants (WWTPs). Alternative technologies may be considered, such as aerobic granular sludge (GAS).@en

Asphalt concrete (AC) modulus reduction caused by repeated axle loading significantly affects pavement long-term performance; including when built on a semi-rigid layer. However, quantifying this effect is challenging. The primary objective of this paper was to monitor and evaluate modulus reduction and fatigue damage accumulation at various AC depths utilizing data obtained from two semi-rigid pavement sections. During loading, a non-destructive method, portable seismic pavement analyzer (PSPA), was used to predict the modulus ratio. PSPA test results show that the damage is nonlinear with respect to the loading passes. Also, depth and AC thickness can influence the development of damage. A developed model showed that it could predict the aforementioned nonlinear relationship. The model parameters can be used to identify the damage level at various AC depths. Unexpected compared with previous understanding, the damage in AC layers was found to increased first, then decreased, and finally increased with the depth. Since PSPA is cheap, portable, and easy to apply, this method to identify the damage level in AC layers is proven to be applicable and practical.@en

A high responsivity and controllable recovery ultraviolet (UV) photodetector based on a tungsten oxide (WO3) gate AlGaN/GaN heterostructure with an integrated micro-heater is reported for the first time. The WO3nanolayer was deposited by physical vapor deposition (PVD) for deep UV absorption and the micro-heater was integrated for chip level heating and cooling. Our device when exposed to UV wavelength exhibits a high responsivity of 1.67 × 104A W−1at 240 nm and a sharp cut-off wavelength of 275 nm. More importantly, the persistent photoconductivity (PPC) effect can be eliminated by a novel method, mono-pulse heating reset (MHR), which consists in applying an appropriate pulse voltage to the micro-heater right after the removal of the UV illumination. The recovery time was reduced from hours to just seconds without reducing the high responsivity and stability of the photodetector. The UV detection, high responsivity, high stability, controllable recovery process and low production cost of GaN-based photodetectors make these devices extremely attractive for several applications, such as fire detection and missile and rocket warning.@en

Ultrafast photodoping of the Mott insulators, possessing strong correlation between electronic and magnetic degrees of freedom, holds promise for launching an ultrafast dynamics of spins which cannot be described in terms of conventional models of ultrafast magnetism. Here we study the ultrafast laser-induced dynamics of the magnetic order in a novel spin-orbit Mott insulator Sr2IrO4 featuring an uncompensated pattern of antiferromagnetic spin ordering. Using the transient magneto-optical Kerr effect sensitive to the net magnetization, we reveal that photodoping by femtosecond laser pulses with photon energy above the Mott gap launches melting of the antiferromagnetic order seen as ultrafast demagnetization with a characteristic time of 300 fs followed by a sub-10-ps recovery. Nonequilibrium dynamical mean-field theory calculations based on the single-band Hubbard model confirm that ultrafast demagnetization is primarily governed by the laser-induced generation of electron-hole pairs, although the precise simulated time dependencies are rather different from the experimentally observed ones. To describe the experimental results, here we suggest a phenomenological model which is based on Onsager's formalism and accounts for the photogenerated electron-hole pairs using the concepts of holons and doublons.@en