Computational geometry

1. Health care is a human right. 2. The care of the individual is at the center of health care, but the whole system needs to work to improve the health of populations. 3. The health care system must treat illness, alleviate suffering and disability, and promote health. 4. Cooperation with each other, those served, and those in other sectors is essential for all who work in health care. 5. All who provide health care must work to improve it. 6. Do no harm.

https://www.journalacs.org/article/S1072-7515(00)00768-7/pdf

Is this a practical approach

Accelerated restructuring in rural China fueled by 'increasing vs. decreasing balance' land-use policy for dealing with hollowed villages

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F 1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet .

DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

Since China initiated economic reforms and an open-door policy in 1978, rural China has made remarkable achievements during the past three decades. The GDP of rural China increased from 2.8 trillion yuan (1 U.S. dollar = 6.7 yuan) in 1996 to 8.8 trillion yuan in 2008, with the annual growth rate at around 10%, and the average annual income per capita in rural China reached 4,761 yuan in 2008, with the growth rate of 8%. However, there are still many biophysical, socio-economic, and political problems or constraints that need to be solved or taken into account for sustainable development in rural China. Among them, rural land issue has always been a concern of the Chinese government and people (Long et al. 2010).

/pdf/land-consolidation-an-approach-for-sustainable-development-27ewyi5lcm.pdf

Land Consolidation: An Approach for Sustainable Development in Rural China

https://escholarship.org/content/qt4m4965p1/qt4m4965p1.pdf?t=pbaoao

Extending the SLEUTH model to integrate habitat quality into urban growth simulation.

Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster.

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Various methods have been developed to detect and assess building’s damages due to earthquakes using remotely sensed data. After the launch of the high resolution sensors such as IKONOS and QuickBird, it becomes realistic to identify damages on the scale of individual building. However the low accuracy of the results has often led to the use of visual interpretation techniques. Moreover, it is very difficult to estimate the degree of building damage (e.g. slight damage, moderate damage, or severe damage) in detail using the existing methods. Therefore, a novel approach integrating LiDAR data and high resolution optical imagery is proposed for evaluating building damage degree quantitatively. The approach consists of two steps: 3D building model reconstruction and rooftop patch-oriented 3D change detection. Firstly, a method is proposed for automatically reconstructing 3D building models with precise geometric position and fine details, using pre-earthquake LiDAR data and high resolution imagery. Secondly, focusing on each rooftop patch of the 3D building models, the pre- and post-earthquake LiDAR points belonging to the patch are collected and compared to detect whether it was destroyed or not, and then the degree of building damage can be identified based on the ratio of the destroyed rooftop patches to all rooftop patches. The novelty of the proposed approach is to detect damages on the scale of building’s rooftop patch and realize quantitative estimation of building damage degree.

Zhenjie Chen

Papers

Land Consolidation: An Approach for Sustainable Development in Rural China

Extending the SLEUTH model to integrate habitat quality into urban growth simulation.

Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster.

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Post-earthquake assessment of building damage degree using LiDAR data and imagery