Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery

Automatic road extraction from satellite imagery using deep learning is a viable alternative to traditional manual mapping. Therefore it has received considerable attention recently. However, most of the existing methods are supervised and require pixel-level labeling, which is tedious and error-prone. To make matters worse, the earth has a diverse range of terrain, vegetation, and man-made objects. It is well known that models trained in one area generalize poorly to other areas. Various shooting conditions such as light and angel, as well as different image processing techniques further complicate the issue. It is impractical to develop training data to cover all image styles. This paper proposes to leverage OpenStreetMap road data as weak labels and large scale satellite imagery to pre-train semantic segmentation models. Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training. Using as much as 100 times more data than the widely used DeepGlobe road dataset, our model with the D-LinkNet architecture and the ResNet-50 backbone exceeds the top performer of the current DeepGlobe leaderboard. Furthermore, due to large-scale pre-training, our model generalizes much better than those trained with only the curated datasets, implying great application potential

The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation

Automated Driving Systems Data Acquisition and Processing Platform

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the sensor data from multiple CAVs and extract the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multiple CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, a post-processing method is proposed to refine trajectories from the object tracking algorithms. Aiming to tackle the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories of the same object. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the post-processing algorithm's improvements in noise level and outlier removal are discussed, confirming the functionality and effectiveness of the proposed holistic data collection and processing platform

The M-T hook structure increases the potency of HIV-1 fusion inhibitor sifuvirtide and overcomes drug resistance

Objectives Peptides derived from the C-terminal heptad repeat (CHR) of HIV-1 gp41 are potent fusion inhibitors. We have recently demonstrated that the unique M-T hook structure preceding the pocket-binding motif of CHR peptide-based inhibitors can greatly improve their antiviral activity. In this study, we applied the M-T hook structure to optimize sifuvirtide (SFT), a potent CHR-derived inhibitor currently under Phase III clinical trials in China. Methods The peptide MT-SFT was generated by incorporating two M-T hook residues (Met-Thr) into the N-terminus of sifuvirtide. Multiple structural and functional approaches were used to determine the biophysical properties and antiviral activity of MT-SFT. Results The high-resolution crystal structure of MT-SFT reveals a highly conserved M-T hook conformation. Compared with sifuvirtide, MT-SFT exhibited a significant improvement in the ability to bind to the N-terminal heptad repeat, to block the formation of the six helix bundle and to inhibit HIV-1 Env-mediated cell fusion, viral entry and infection. Importantly, MT-SFT was fully active against sifuvirtide- and enfuvirtide (T20)-resistant HIV-1 variants and displayed a high genetic barrier to developing drug resistance. Conclusions Our studies have verified that the M-T hook structure offers a general strategy for designing novel HIV-1 fusion inhibitors and provide new insights into viral entry and inhibitio

Biotransformation of rare earth oxide nanoparticles eliciting microbiota imbalance

Background Disruption of microbiota balance may result in severe diseases in animals and phytotoxicity in plants. While substantial concerns have been raised on engineered nanomaterial (ENM) induced hazard effects (e.g., lung inflammation), exploration of the impacts of ENMs on microbiota balance holds great implications. Results This study found that rare earth oxide nanoparticles (REOs) among 19 ENMs showed severe toxicity in Gram-negative (G−) bacteria, but negligible effects in Gram-positive (G+) bacteria. This distinct cytotoxicity was disclosed to associate with the different molecular initiating events of REOs in G− and G+ strains. La2O3 as a representative REOs was demonstrated to transform into LaPO4 on G− cell membranes and induce 8.3% dephosphorylation of phospholipids. Molecular dynamics simulations revealed the dephosphorylation induced more than 2-fold increments of phospholipid diffusion constant and an unordered configuration in membranes, eliciting the increments of membrane fluidity and permeability. Notably, the ratios of G−/G+ reduced from 1.56 to 1.10 in bronchoalveolar lavage fluid from the mice with La2O3 exposure. Finally, we demonstrated that both IL-6 and neutrophil cells showed strong correlations with G−/G+ ratios, evidenced by their correlation coefficients with 0.83 and 0.92, respectively. Conclusions This study deciphered the distinct toxic mechanisms of La2O3 as a representative REO in G− and G+ bacteria and disclosed that La2O3-induced membrane damages of G− cells cumulated into pulmonary microbiota imbalance exhibiting synergistic pulmonary toxicity. Overall, these findings offered new insights to understand the hazard effects induced by REOs

Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes)

Impact of Water Level Fluctuations on Landslide Deformation at Longyangxia Reservoir, Qinghai Province, China

The construction of Longyangxia Reservoir has altered the hydrogeological conditions of its banks. Infiltration and erosion caused by the periodic rise and fall of the water level leads to collapse of the reservoir banks and local deformation of the landslide. Due to heterogeneous topographic characteristics across the region, water level also varies between different location. Previous research on the influence of fluctuations in reservoir water level on landslide deformation has focused on single-point monitoring of specific slopes, and single-point water level monitoring data have often been used instead of water level data for the entire reservoir region. In addition, integrated remote sensing methods have seldom been used for regional analysis. In this study, the freely-available Landsat8 OLI and Sentinel-2 data were used to extract the water level of Longyangxia Reservoir using the NDWI method, and Sentinel-1A data were used to obtain landslide deformation time series using SBAS-InSAR technology. Taking the Chana, Chaxi, and Mangla River Estuary landslides (each having different reservoir water level depths) as typical examples, the influence of changes in reservoir water level on the deformation of three wading landslides was analyzed. Our main conclusions are as follows: First, the change in water level is the primary external factor controlling the deformation velocity and trend of landslides in the Longyangxia Reservoir, with falling water levels having the greatest influence. Second, the displacement of the Longyangxia Reservoir landslides lags water level changes by 0 to 62 days. Finally, this study provides a new method applicable other areas without water level monitoring data

Effect of Soluble Salt Loss via Spring Water on Irrigation-Induced Landslide Deformation

Landslide exposes the previously blocked groundwater discharge. High concentrations of soluble salt form salt sinters that can be observed near discharge passages. Based on existing laboratory investigation results of soil leaching and shearing reported in the literature, the effect of the soluble salt loss via spring water on irrigation-induced landslide deformation was studied under large-scale conditions. During our field investigation of landslides in the Heitai terrace of the Yellow River’s upper reaches in Gansu Province, China, 35 spring outlets were found, and the Heitai terrace was divided into five subareas, based on the difference in spring flow. Deformation data for the terrace were obtained by small baseline subset technology (SBAS-InSAR). These data were analyzed in combination with the amount of soluble salt loss, to explore the correlation between the deformation of the landslide and the soluble salt loss in the loess irrigation area. The results showed that the cumulative deformation and the loss of soluble salt were increasing continuously in the terrace. Although the increasing intensity of each subarea was different, the changing intensity of the two during the corresponding monitoring period was highly consistent. The statistical analysis revealed a strong positive correlation between the accumulated loss of soluble salt via spring water and the accumulated displacement of the terrace edge (p < 0.01). After the slope k between the two was tested by the Grubbs test and t-test, the k was no abnormality (α = 0.05) and difference (Sig > 0.05), further providing the basis for confirming the existence of this positive correlation. When the loss of soluble salt in rock and soil increased gradually, the accumulated deformation of the terrace edge also increased continuously. The findings of this study are of great significance for understanding the formation mechanism of landslides and the identifying landslide revival in irrigation areas of the Loess Plateau