Built environment stocks have attracted much attention in recent decades because of their role in material and energy flows and environmental impacts. Spatially refined estimation of built environment stocks benefits city management, for example, in urban mining and resource circularity strategy making. Nighttime light (NTL) data sets are widely used and are regarded as high-resolution products in large-scale building stock research. However, some of their limitations, especially blooming/saturation effects, have hampered performance in estimating building stocks. In this study, we experimentally proposed and trained a convolution neural network (CNN)-based building stock estimation (CBuiSE) model and applied it to major Japanese metropolitan areas to estimate building stocks using NTL data. The results show that the CBuiSE model is capable of estimating building stocks at a relatively high resolution (approximately 830 m) and reflecting spatial distribution patterns, although the accuracy needs to be further improved to enhance the model performance. In addition, the CBuiSE model can effectively mitigate the overestimation of building stocks arising from the blooming effect of NTL. This study highlights the potential of NTL to provide a new research direction and serve as a cornerstone for future anthropogenic stock studies in the fields of sustainability and industrial ecology.

<jats:p>Fill material flows created by land development earthworks are anthropogenic agents that generate massive energy use from their heavy loads. However, formal quantification of these flows has been neglected. We use Osaka Prefecture in Japan as a case study to quantify fill flows and associated CO2 emissions. We collected data on fill flows, including fill generation and acceptance. We mapped these publicly uncounted fill flows and calculated the CO2 emissions from the associated energy use. We also simulated a scenario in which optimized shortest-distance matching is achieved between fill generators and acceptors. We estimated the current fill flows based on distance and weight and broke down the total by type of site and activity. We compared our estimates of current fill flows with estimates from our matching simulation and found the simulation could achieve an 8448 km reduction in flow length and a 5724 t-CO2 reduction in emissions associated with transportation. We discussed the implications of flexible matching, especially in different construction sectors, and the importance of continuous, spatially geo-referenced monitoring of these fill flows toward further environmental impact mitigation. The approach presented here could apply to assessing environmental loads arising from landform changes in other cities and lead to development of a new regional- and global-scale fill material science in the Anthropocene.</jats:p>

In this study, we developed a model to estimate the disaster waste generation values in municipalities. The estimated values will be used to formulate or revise disaster waste disposal plans drawn by municipalities as countermeasures against the Nankai Trough Earthquake, especially for those areas that require special strengthening. We also used the model to carefully examine all the points that must be considered when estimating the amount of disaster waste generation. In addition, inland municipalities where the values are smaller than existing disaster waste generation values and those whose values are higher were extracted.

 The results showed that existing disaster waste generation values do not necessarily match the situation of municipalities. Thereafter, we proposed an effective way to formulate or revise future disaster waste disposal plans.

In order to achieve the recycling society worldwide, it is important to analyze material stock and flow on a global scale and understand their accumulation trends and patterns. World-wide satellite images data is easily and quickly accessible, and it breaks the restriction of statistical data. Recently, Convolutional Neural Network (CNN) is received a lot of attentions as a technique in the analysis of satellite images. Our study made efforts to develop an advanced model for estimating the total floor area of buildings based on night light data using CNN. Three major metropolitans in Japan (Tokyo, Osaka, and Nagoya metropolitan area) were selected as the estimation areas and the learning areas, setting one metropolitan as learning area to estimate the buildings floor areas for the other two. And same procedure has been done three times as each metropolitan was changed to be learning area in turns. Additionally, we verified the accuracy of results to examine the effectiveness of our model. In the case of “Osaka learning for Nagoya estimation”, it was clear that the total floor area of buildings in Nagoya metropolitan area was 450 million m², which is close to the real value and indicates our model can be useful?