Simultaneously mapping the 3D distributions of multiple heavy metals in an industrial site using deep learning and multisource auxiliary data.

Three-dimensional (3D) distributions of multiple soil pollutants in industrial site are crucial for risk assessment and remediation. Yet, their 3D prediction accuracies are often low because of the strong variability of pollutants and availability of 3D covariate data. This study proposed a patch-based multi-task convolution neural network (MT-CNN) model for simultaneously predicting the 3D distributions of Zn, Pb, Ni, and Cu at an industrial site.

Localization and recognition of human action in 3D using transformers.

Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization.

Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline.

Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV solutions either require considerable resources to execute on-vehicle inference or suffer from modest performance. This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing faster BEV perception on the on-vehicle chips.

Centennial Records of Microplastics in Lake Cores in Huguangyan Maar Lake, China.

Microplastic records from lake cores can reconstruct the plastic pollution history. However, the associations between anthropogenic activities and microplastic accumulation are not well understood. Huguangyan Maar Lake (HML) is a deep-enclosed lake without inlets and outlets, where the sedimentary environment is ideal for preserving a stable and historical microplastic record.

Delving Into the Devils of Bird's-Eye-View Perception: A Review, Evaluation and Recipe.

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view.

CycleMLP: A MLP-Like Architecture for Dense Visual Predictions.

This article presents a simple yet effective multilayer perceptron (MLP) architecture, namely CycleMLP, which is a versatile neural backbone network capable of solving various tasks of dense visual predictions such as object detection, segmentation, and human pose estimation. Compared to recent advanced MLP architectures such as MLP-Mixer (Tolstikhin et al. 2021), ResMLP (Touvron et al.

Three-dimensional spatial prediction of Zn in the soil of a former tire manufacturing plant using machine learning and readily attainable multisource auxiliary data.

Pollutants in the soil of industrial site are often highly heterogeneously distributed, which brought a challenge to accurately predict their three-dimensional (3D) spatial distributions. Here we attempt to create effective 3D prediction models using machine learning (ML) and readily attainable multisource auxiliary data for improving the prediction accuracy of highly heterogeneous Zn in the soil of a small-size industrial site. Using raw covariates from functional area layout, stratigraphic succession, and electrical resistivity tomography, and derived covariates of the raw covariates as predictors, we created 6 individual and 2 ensemble models for Zn, based on ML algorithms such as k-nearest neighbors, random forest, and extreme gradient boosting, and the stacking approach in ensemble ML.

Deeply Unsupervised Patch Re-Identification for Pre-Training Object Detectors.

Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning global representations for image-level classification tasks instead of discriminative local region representations, which limits their transferability to region-level downstream tasks, such as object detection. To improve the transferability of pre-trained features to object detection, we present Deeply Unsupervised Patch Re-ID (DUPR), a simple yet effective method for unsupervised visual representation learning.

Spatiotemporal variation and sources of soil heavy metals along the lower reaches of Yangtze River, China.

Excessive accumulation of soil heavy metals (HMs) result in the deterioration of soil quality and reduction of agricultural productivity and safety. The accumulation status, temporal change, and sources of soil HMs were determined by large-scale field surveys in 2014 and 2019 in rapid urbanization and industrialization area along the lower reaches of the Yangtze River, China. Eighty-two surface soil samples were collected in 2014 and ninety-five surface soil samples and seven soil profiles (0-100 cm) were collected in 2019.

PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond.

Reducing complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this problem by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask++, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, leading to several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity.

PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text.

Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes.

Spatio-Temporal Change and Pollution Risk of Agricultural Soil Cadmium in a Rapidly Industrializing Area in the Yangtze Delta Region of China.

The impacts of rapid industrialization on agricultural soil cadmium (Cd) accumulation and their potential risks have drawn major attention from the scientific community and decision-makers, due to the high toxicity of Cd to animals and humans. A total of 812 topsoil samples (0⁻20 cm) was collected from the southern parts of Jiangsu Province, China, in 2000 and 2015, respectively. Geostatistical ordinary kriging and conditional sequential Gaussian simulation were used to quantify the changes in spatial distributions and the potential risk of Cd pollution between the two sampling dates.

Soil mercury accumulation, spatial distribution and its source identification in an industrial area of the Yangtze Delta, China.

Understanding soil mercury (Hg) accumulation, spatial distribution, and its sources is crucial for effective regulation of Hg emissions. We chose a study area covering approximately 100 km representing one of the rapid growing industrial towns of the Yangtze River Delta (YRD), China, to explore soil Hg accumulation. In surface soil, total Hg ranged from 310 to 3760 μg/kg, and 53% samples exceeded the most generous Chinese soil critical value (1500 µg/kg).