Discussions about Magic Weapon of CPC's Work Based on Study of Mao Zedong's Rural Surveys

Former chairman Mao Zedong conducted extensive rural surveys during the exploration of the Chinese revolutionary road. In 1941,in order to change the state of the party's theory separating from reality,Mao Zedong organized and published the book Rural Surveys,which provided the direction for survey and research of the party members. The survey and research are excellent tradition and style of work of the party,and also a major weapon for outstanding achievements in China's revolution and construction. Socialism with Chinese characteristics has entered a new era. Studying the Rural Surveys has important practical value for beating off the new challenges in the issues concerning agriculture,farmers,and rural areas.

Rural Survey of Agricultural College of University of Nanking in the Republic of China and Its Realistic Enlightenment

In the new era of implementing the rural revitalization strategy,it is necessary and indispensable to review and sort out the rural survey of the Agricultural College of University of Nanking and think about its beneficial enlightenment.

Chen Yun's Rural Surveys in China

In order to explore a way to completely transform China,the Chinese Communists have vigorously promoted the style of investigations and research in rural areas.Chen Yun is a model of this rural investigation movement.He focused on understanding the life of the peasant class and the reality of China's rural areas through field investigations.The survey content was meticulous and extensive,and the survey process sought truth from facts.He creatively used a variety of survey methods based on objective conditions,and strove to earnestly benefit the people.Chen Yun provided Marxist guiding ideology for rural investigations in China,guided rural investigations to proceed on the right track,and at the same time infused rural investigations with the value concept of putting the people first,which still has important reference significance for solving the problems of agriculture,rural areas and farmers in the new situation.

Establishment of Rural Housing Land Standard in China

On the basis of rural household survey in 12 provinces of China in 2005,this research built an econometrical model to find the area standard for rural housing land.This standard is expected to facilitate rural housing land administration,efficient and intensive housing land use and policy making.This research concludes:1) according to the household survey data,the average area for rural housing land in China is about 235.26 m 2,and the rural housing land mainly includes unused land(42.89%) and house construction land(37.76%) with the average floor-area ratio of 0.42;2) the indexes in the standard concern the factors such as location,landform,house form,household population and cultivated land area per farmer,which all have significant effects on rural housing land use;3) the reasonable area for rural housing land may be 150 m 2 in average before 2020,and the referential standards in different provinces are 120 m 2,130 m 2,140m 2,150 m 2,160 m 2,170 m 2,190 m 2,200 m 2,210 m 2,220 m 2,230 m 2 and 250 m 2 respectively;and 4) if there are less than two persons or more than five persons in a family,these standards need to be decreased or increased by 7%.When the cultivated land area per farmer is lower than the provincial average,the standards have to reduce by 3.5%.

Automatic classification of rural building characteristics using deep learning methods on oblique photography

Rural building is important to the well-being of rural residents,leading to a significant need to carry out extensive surveys and retrofits of many rural buildings.On-site surveys by expert surveyors are currently the main approach,but this traditional method is often expensive and laborious,especially for large-scale survey tasks.Therefore,this study explores an alternative workflow based on deep learning(DL)methods to apply automatic classification of rural building characteristics.Taking four villages in Jizhou District of Tianjin,China as research samples,we tested selected convolutional neural network(CNN)architectures through the establishment of the training database containing 3258 labeled images,under the performance metrics of accuracy,recall and F1 score.The results showed that ResNet50 is the CNN architecture with the best performance,with the comprehensive consideration of overall metrics.Taking accuracy as the performance metric to test the generalization ability of ResNet50,the prediction results for seven building characteristic indicators from low to high are as follows:building function(0.827);building style(0.863);building quality(0.871);building age(0.880);building structure(0.891);abandoned or not(0.959);the number of storeys(0.995).Due to simplicity,accuracy and effectiveness,this workflow is transferable and cost-effective to investigate large-scale villages.

An overview report on the situation of elderly people in urban and rural China:2000-2015

China had become a country with an aging population by the end of the 20th century.Dealing with the aging of its population is a long-term strategic task for China.“The National Survey on Elderly People in Urban and Rural China”has been carried out four times since the year 2000.The data shows that during the years 2000 to 2015 the wellbeing of elderly people in urban and rural China improved greatly.The economic status of the elderly has obviously improved;and there have also been steady improvements to social security protections,health care services and the availability of social activities in which the elderly can participate.In addition,the spiritual and cultural life of the elderly has become richer and more colorful.The aging of society creates challenges,such as potential shortages of labor resources,the weakening of family functions and the need for more medical care facilities and services,and these intensify over time.At the same time,changes to the structure of demand and of industry caused by societal aging bring many opportunities.