CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence.

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content.

Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data.

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan.

Assessing urban wetlands dynamics in Wuhan and Nanchang, China.

Urban wetlands play a crucial role in sustainable social development. However, current research mainly focuses on specific wetland types, and fine extraction of urban wetlands remains a challenge. This study proposes a fine extraction framework based on hierarchical decision trees and shape features for urban wetlands, using Sentinel-2 remote sensing data to obtain detailed wetland data of Wuhan and Nanchang from 2016 to 2022.

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity.

Continuous urban expansion has a negative impact on the potential of terrestrial vegetation. Till now, the mechanism of such impact remains unclear, and there have been no systematic investigations. In this study, we design a theoretical framework by laterally bridging urban boundaries to explain the distress of regional disparities and longitudinally quantify the impacts of urban expansion on net ecosystem productivity (NEP).

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanization has become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data.

Assessing the impact of drought-land cover change on global vegetation greenness and productivity.

Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring.

Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence.

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend.

Enhanced image prior for unsupervised remoting sensing super-resolution.

Numerous approaches based on training low-high resolution image pairs have been proposed to address the super-resolution (SR) task. Despite their success, low-high resolution image pairs are usually difficult to obtain in certain scenarios, and these methods are limited in the actual scene (unknown or non-ideal image acquisition process). In this paper, we proposed a novel unsupervised learning framework, termed Enhanced Image Prior (EIP), which achieves SR tasks without low/high resolution image pairs.

Arsenic in a groundwater environment in Bangladesh: Occurrence and mobilization.

Groundwater with an excessive level of Arsenic (As) is a threat to human health. In Bangladesh, out of 64 districts, the groundwater of 50 and 59 districts contains As exceeding the Bangladesh (50 μg/L) and WHO (10 μg/L) standards for potable water. This review focuses on the occurrence, origin, plausible sources, and mobilization mechanisms of As in the groundwater of Bangladesh to better understand its environmental as well as public health consequences.

User Identification across Asynchronous Mobility Trajectories.

With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories.

High arsenic contamination and presence of other trace metals in drinking water of Kushtia district, Bangladesh.

Drinking water with excessive concentration levels of arsenic (As) is a great threat to human health. A hydrochemical approach was employed in 50 drinking water samples (collected from Kushtia district, Bangladesh) to examine the occurrence of geogenic As and the presence of trace metals (TMs), as well as the factors controlling As release in aquifers. The results reveal that the drinking water of shallow aquifers is highly contaminated by As (6.

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China.

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low.

Thin cloud removal from single satellite images.

A novel method for removing thin clouds from single satellite image is presented based on a cloud physical model. Given the unevenness of clouds, the cloud background is first estimated in the frequency domain and an adjustment function is used to suppress the areas with greater gray values and enhance the dark objects. An image, mainly influenced by transmission, is obtained by subtracting the cloud background from the original cloudy image.

Invariant matching method for different viewpoint angle images.

In recent years, many methods have been put forward to improve the image matching for different viewpoint images. However, these methods are still not able to achieve stable results, especially when large variation in view occurs. In this paper, an image matching method based on affine transformation of local image areas is proposed.

Fusion of infrared and visible images based on focus measure operators in the curvelet domain.

Aiming at the differences of physical characteristics between infrared sensors and visible ones, we introduce the focus measure operators into the curvelet domain in order to propose a novel image fusion method. First, the fast discrete curvelet transform is performed on the original images to obtain the coefficient subbands in different scales and various directions, and the focus measure values are calculated in each coefficient subband. Then, the local variance weighted strategy is employed to the low-frequency coefficient subbands for the purpose of maintaining the low-frequency information of the infrared image and adding the low-frequency features of the visible image to the fused image; meanwhile, the fourth-order correlation coefficient match strategy is performed to the high-frequency coefficient subbands to select the suitable high-frequency information.

Color constancy enhancement under poor illumination.

In this Letter, the color constancy and its realization were studied and a novel color constancy image enhancement algorithm under poor illumination was presented. The purpose of this algorithm is to maintain the hue of an image during the processing so that the change of saturation can be minimized. The original image was first multiplied by a scale parameter obtained by the adaptive quadratic function to enhance the luminance, and then the edge details were restored by a shifting parameter.