China is a major maritime country with numerous islands, which are rich in natural resources. Island resources exhibit excellent development potential; in this regard, the market demand for uninhabited island development has been strong. The scientific and reasonable utilization of the resources of uninhabited islands can create huge economic value for the region and the country, inject vitality into the national economy, and enhance the stability of the overall sustainable development of the national economy. However, previous research on islands focused on a limited area of economy or ecology, and few studies provide a comprehensive evaluation of uninhabited island development. Such development requires enormous investment and has a profound impact. Therefore, a comprehensive and scientific evaluation system is necessary for uninhabited island development planning. This paper accordingly develops an island planning and evaluation indicator system based on multi-criteria decision-making (MCDM), and entropy analysis method, and the approach of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A validation analysis was conducted to prove the effectiveness of the evaluation system. The evaluation system comprehensively considers both economic and ecological factors, which makes up for the one-sidedness of the previous evaluation focusing on economy or ecology, and effectively ensures the sustainable development of island development. In this regard, the evaluation system can provide important guidance for the government’s island planning management and the investment decision-making of enterprises. This system can also provide new ideas for the sustainable development of islands.

Sewer pipe is an essential infrastructure in the city as it undertakes the transportation and circulation of wastewater resources. But sewer pipe it is easy to have faults and cause serious secondary urban accidents, such as road holes and road collapse. Because of the complex underground circumstance, inspecting large-area sewer pipes using closed-circuit television or periscope television is difficult. In this study, we proposed a collaborative sewer pipe inspection approach by using novel low-cost pipe robotic capsules, which capture the images of the pipeline inner walls when floating with the water flow. A set of workers collaboratively drop and salvage capsules to cover a large-area pipe network. The routes of workers and pipe capsules are optimized by a meta-heuristic algorithm integrating local search and simulated annealing. The deep neural network is used to recognize faults from raw captured images. A field experiment in Shenzhen was conducted to evaluate the performance of the proposed approach. The results demonstrate that it outperforms the naive inspection method with a shorter travel distance and less waiting time. It is also effective for inspecting the large-area sewer pipe networks with an overall precision of 0.92. It will help us to eliminate the potential safety risk of the public and promote the level of urban governance.