Livability is one of the major guiding principles for urban planning and policymaking, of which the definition and evaluation have become the crucial research topic. As the progress in socioeconomic development accelerates, the microscale living conditions require more urgent attention. However, few researchers have addressed the assessment of urban livability at a finer spatial scale such as the community scale. Thus, this article aims to evaluate the urban environmental quality at the community level given the residential community as the basic unit of urban living areas. We select eighteen objective indicators from five dimensions to establish an objective indicator system. Taking the preferences of different age groups into account, a comprehensive evaluation framework for the livability of communities combining both subjective perceptions and objective indicator is constructed. Then, it is applied to evaluate the livability of 1,394 residential communities in Ningbo City. There are three significant results from the study. First, different age groups have diverse preferences of demands to the livability of an urban community. The indicators they valued most concentrated in the following two dimensions: the convenience of transportation and the completeness of supporting facilities. Second, there exists significant heterogeneity in the livability of communities among districts. Third, the livability of communities shows a decreasing spatial pattern from the city center to the surroundings. These empirical results can be advantageous to urban planning departments and other relevant stakeholders.
Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. Although these traditional methods can achieve target recognition, they are inefficient and cannot meet the high time efficiency requirements of disaster relief. In this paper, we combined an object detection model with a generative adversarial network model to build a two-stage deep learning model for sensitive target detection and hiding in remote sensing images, and we verified the model performance on the aircraft object processing problem in remote sensing mapping. To improve the experimental protocol, we introduced a modification to the reconstruction loss function, candidate frame optimization in the region proposal network, the PointRend algorithm, and a modified attention mechanism based on the characteristics of aircraft objects. Experiments revealed that our method is more efficient than traditional manual processing; the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score is 44% higher than that of the original model. In addition, our method can quickly and intelligently detect and hide sensitive targets in remote sensing images, thereby shortening the time needed for emergency mapping.
Urban land-use scene classification from high-resolution remote-sensing imagery at high quality and accuracy is of paramount interest for urban planning, government policy-making and urban change detection. In recent years, urban land-use classification has become an ongoing task in areas addressable primarily by remote sensing, and numerous deep learning algorithms have achieved high performance on this task. However, both dataset and methodology problems still exist in the current approaches. Previous studies have relied on limited data sources, resulting in saturated classification results, and they have difficulty achieving comprehensive classification results. The previous methods based on convolutional neural networks (CNNs) focused primarily on model architecture rather than on the hyperparameters. Therefore, to achieve more accurate classification results, in this study, we constructed a new large dataset for urban land-use scene classification. More than thirty thousand remote sensing scene images were collected to create a dataset with balanced class samples that includes both higher intra-class variations and smaller inter-class dissimilarities than do the previously available public datasets. Then, we analysed two possible strategies for exploiting the capabilities of three existing popular CNNs on our datasets: full training and fine tuning. For each strategy, three types of learning rate decay were applied: fixed, exponential and polynomial. The experimental results indicate that fine tuning tends to be the best-performing strategy, and using ResNet-V1-50 and polynomial learning rate decay achieves the best results for the urban land-use scene classification task.
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