Blue steel panels have the advantages of being lightweight, easy-to-install, cost-effective, and fireproof [1
]. With these economic attributes, blue steel panels have been widely used in roof construction in many inefficient industrial areas (factories and warehouses) and gymnasiums [2
]. The distribution of blue steel panels also, directly and indirectly, reflects the urban industrial structure and economic development. For example, the construction of blue steel roofs is largely related to inefficient industrial land [3
]. In addition, blue steel roofs are also an important part of urban surface areas. Their wide application not only brings convenience to production and life but also negative effects, such as the urban heat island effect. Some researchers have explored the relationship between blue steel roofs and the urban heat environment. For example, some studies have demonstrated a positive correlation between the proportion of blue steel roofs and land surface temperature with an R2
of 0.71 [5
]. Thus, information on blue steel roofs can provide data to support the study of urban industrial structure and also contribute to the study of urban ecology.
Currently, remote sensing technology is a powerful tool that provides detection and monitoring information for blue steel roofs. These methods include an object-oriented method [6
] and spectral index [7
] etc. However, within these methods, limitations still exist which influence accuracy. For example, Li [6
] analyzed colored steel sheds by an object-oriented model based on Gaofen-1 image and showed that the extraction accuracy in two experimental areas was greater than 88%. The setting of separation scale in image segmentation needed a lot of repeat tests. In addition, the shape of the segmented results could not be perfectly matched the shape of the objects, reducing the extraction accuracy. Guo et al. [7
] developed a BSTBI (blue steel tile building index) to extract blue steel roofs using TM (Thematic Mapper) images with the aid of the spectral characteristics of them. The overall accuracy was greater than 85%, while old blue steel tiles covered with dust, along with the lack of homogeneity in meteorological factors, suppressed the performance of indexes and affected the threshold settings associated with the results, thus reducing the accuracy of extraction. Compared with the above methods, deep learning would be a better choice because it has a more powerful and abstract learning ability and higher image recognition accuracy which may improve the weak automation in the object-oriented method. On the other hand, deep learning method can weaken the influence of dust and meteorological factors by increasing the samples of the training dataset.
Hinton et al. [8
] proposed the concept of a deep frame neural network, this network showed improved performance and reduced complexity of image segmentation [9
]. The deep learning model has been widely applied in geography, medicine, and physics [10
]. For image recognition applications, the most important network structure in the deep learning algorithm is the CNN (Convolutional Neural Network) structure, which has the advantage of enabling computers to automatically extract feature information [19
]. Many groups of researchers have begun to use CNN in many applications with impressive performance, such as image classification [20
], object recognition [22
], land use [24
], and semantic segmentation [26
With the increasing demand for practical work in recent years, deep semantic segmentation algorithms have been widely used in remote sensing image processing. The DeeplabV3+ [28
] model developed by Google in 2018 is an example of a deep learning algorithm. From the fully convolutional network proposed in 2014 [26
] to the DeeplabV3+ in 2018 in the field of image semantic segmentation, the detection effect and performance of these algorithms on public natural scene data sets have increased. Specifically, the Mean Intersection Over Union of the DeeplabV3+ algorithm in the public dataset PASCAL VOC 2012 reached 89%, which is a significant improvement over the previous algorithm [29
]. Thus, the use of DeepLabV3+ for remote sensing image segmentation has received increased attention by researchers [30
For example, Wang and Li [31
] used public datasets for model training, applied the DeeplabV3+ network to road network recognition and found that the road extraction accuracy could reach 77.2% at a single scale. In addition, Fang [33
] generated a dataset based on Google Earth and also applied the DeepLabV3+ to road network extraction, achieving an accuracy of 86.06%. Liu et al. [30
] improved the network in light of the deficiencies of the DeeplabV3+ network, and the accuracy of verification in the high-resolution remote sensing image dataset reached 85%. Tang et al. [34
] employed the DeeplabV3+ model and the traditional supervised classification method to extract grassland information simultaneously and found that the DeeplabV3+ extraction accuracy could reach 79.82%, which is higher than the traditional supervised classification method by 5%. Under continuous experiment and verification of a large number of datasets, the segmentation results based on the DeeplabV3+ network had a higher accuracy and a more significant effect.
The Gaofen-2 remote sensing images of the Nanhai District (Lishui, Dali, Shishan, Guicheng) of Foshan, Guangdong Province, China that recorded in 2016 have been used to extract blue steel roofs information based on the DeepLabV3+ deep learning model, followed by a discussion of the patterns of the spatial distribution of blue steel roofs and influencing factors. The findings provide important data for enhancing the ability to identify types of industrial areas with blue steel roofs and could also be used to enhance the construction and management of urban settlements.
4.1. Accuracy Evaluation
The four accuracy evaluation indexes described above were used to estimate the accuracy of 20 areas for verification. The value of each index ranged between 0 and 1, with larger values corresponding to higher accuracy. The accuracy verification results of the four indicators and the average value of each indicator are shown in Figure 6
The mean values of precision and recall were 0.81 and 0.84, respectively. The mean value of the F1-score of the 20 samples was 0.82, meaning that the classifier has good performance. This result can also be incarnated in the 800 samples which were used as validation; the accuracy of the DeepLabV3+ model is 70%. However, the mean value of accuracy was 0.92, indicating that a high extraction accuracy was achieved. In the 20 validation samples, there were eight samples with accuracy values higher than 0.9, and only one sample had an accuracy lower than 0.6. Recall values for 12 samples were above 0.9 and below 0.6 for four samples. Overall, there were six samples wherein all four indexes had values above 0.9, and the tenth sample had the lowest values for all four indicators.
4.2. Spatial Distribution of Blue Steel Roofs
The mean center is calculated by ArcGIS software according to Equation (7) and the weighted mean center according to Equation (8). Figure 7
, the map of the spatial distribution of blue steel roofs and the center reveals that the spatial distribution of the blue steel roofs showed some degree of clustering and uneven distribution at the scale of the study area and that the distribution was concentrated in Shishan. At the town scale, the deviation in the mean center and distribution center was not large. The blue steel roofs within the Guicheng, Dali, and Lishui regions were evenly distributed, and the distribution type of Shishan was generally uniform.
The total area of blue steel roofs was 17.84 km2. If the blue steel roofs area was allocated proportionally to each town (i.e., the geographical area of each town accounted for the total geographic area of the research area)—2.27 km2 (Guicheng), 2.48 km2 (Dali), 4.01 km2 (Lishui), and 9.09 km2 (Shishan)—then was 58.76. However, the value of , when the proportion of the different cites is not accounted for, was 62, indicating that the blue steel roofs distribution in this area was relatively clustered. of the blue steel roofs in the study area was 0.17, indicating that the distribution of the blue steel roofs was uneven.
of each town were also calculated separately to explore the distribution types of blue steel roofs within towns themselves (Table 4
of Shishan was 9.36, which was the highest among the four towns.
of Shishan Town was 0.41, corresponding to a generally uniform spatial distribution type. With the exception of Shishan,
was higher than 0.6, and the distribution type was uniform.
4.3. Area of Blue Steel Roofs
The study area included four towns—Shishan, Dali, Lishui, and Guicheng—and the total geographical area was 662.37 km2
. The town with the largest geographical area (Shishan) was 337.36 km2
, followed by Lishui (148.82 km2
) (Figure 8
). The town with the smallest geographical area was Guicheng (84.28 km2
), followed by Dali (91.92 km2
). Based on the vector statistics in ArcGIS, the town with the largest area of blue steel roofs was Shishan (10.02 km2
), followed by Dali (3.39 km2
). The town with the smallest area was Guicheng (1.54 km2
), followed by Lishui (2.89 km2
). The proportion of blue steel roofs out of the total area was 8.62% (Guicheng), 16.20% (Lishui), 19.01% (Dali), and 56.16% (Shishan), respectively. If the ratio of the area of blue steel roofs to the total geographical area is assumed to represent the average density of blue steel roofs of Guicheng, Dali, Lishui, and Shishan, then the average density of blue steel roofs in the four towns was 1.83%, 3.68%, 1.94%, and 2.97% respectively. Dali is the town with the highest average density of blue steel roofs, and Guicheng is the town with the lowest average density of blue steel roofs. Thus, Shishan accounts for more than half of the blue steel roofs in the study area, and the other three towns account for less than 50%.
Calculations of the geographical area and blue steel roofs area of the villages show that Jianxing in Lishui, is the village with the lowest average density of blue steel roofs. The area of blue steel roofs was only 0.0012 km2
, and the average density was 0.02%. The village with the highest average density of blue steel roofs was Xingxian in Shishan (9.50%). Figure 9
shows the proportion of the blue steel roofs area relative to the total blue steel roofs area. Among areas of blue steel roofs for each village, Shishan had the largest proportion, followed by Dali. Shishan not only had a large blue steel roofs area but also had an average density of blue steel roofs. Among all towns, Shishan had a higher number of large blue steel roofs buildings relative to the other towns.
In addition, the area of blue steel roofs is also closely related to the study of inefficient industrial urban land. In Nanhai, Shunde, and other places, the proportion of urban construction land has exceeded 40% and is only 21% in Hong Kong and 16.4% in Japan’s three metropolitan areas. Therefore, if the construction of urban land is not regulated and managed, available land may be depleted [47
]. In 2016, the former Ministry of Land and Resources issued a notice of “guiding opinions on further promoting the redevelopment of urban low utility land (implementation)”. To date, nationwide research and verification of the low efficiency of urban land construction have been conducted on a large scale. For industrial land, detailed identification and evaluation would greatly improve the development and future direction of urban development, as the fundamental purpose is to promote the sustainable use of land resources along with conservation and intensive use [48
]. The identification of inefficiently designed industrial land can be made based on considering three aspects: land production efficiency, the utilization rate of industrial land, and the adequacy of social service function [49
]. The evaluation indicators can be determined based on the aforementioned ideas relating to low-efficiency industrial land evaluation. Among these indexes, there is a need to make calculations according to the area of the industrial land building, such as the average output intensity of the land, the average tax of the land, and the provision of jobs per unit area. For some heavy industrial plants using steel frame structures, the area of blue steel roofs can reflect the area of industrial land, which is important for conducting preliminary assessments of areas of inefficient industrial land and for regional planning and coordination.
4.4. Correlation with Social Economic Data
GVIOADS, RLF, ICECADS, Population, and GVIO were analyzed as potential factors that could explain the distribution of blue steel roofs. The results of the correlation analysis are shown in Table 5
Population and blue steel roofs area showed a weak positive relationship (r = 0.487, p = 0.513). GVIOADS, RLF, and GVIO all showed strong positive correlations with blue steel roofs area (r = 0.988, p = 0.012; r = 0.971, p = 0.029; and r = 0.985, p = 0.015, respectively). ICECADS also was highly and significantly positively correlated (r = 0.995, p = 0.005).
With the development of the construction industry, colored steel plates have evolved with the development of steel structures and have gradually replaced the traditional building structures, and have come to be widely used in major industrial buildings [50
]. In some heavy industries, such as metallurgy, machinery, and automobile manufacturing, where large and medium-sized machine tools and complete sets of equipment are used, plants are generally constructed with a single-layer frame structure to meet the requirements of placing large and heavy equipment in the workshop to produce heavy products [52
]. Nowadays, the roofs of these steel-structured industrial plants generally use colored steel panels, such as single-layer colored steel roofs or double-layer colored steel tile on-site composite glass wool roofs [53
Among correlations between the area of blue steel roofs and potential influencing factors, the correlation between ICECADS and the area was the highest, followed by GVIOADS. Because some heavy industrial plants need to use colored steel roofs with steel frames, the area of blue steel roofs is closely related to ICECADS and GVIOADS. According to the Statistical Yearbook of the Nanhai District in 2016, the GVIO of heavy industry accounted for 66.28% of the GVIO of Nanhai District, and the Industry Energy Consumption of heavy industry accounted for 77.88% of Nanhai District.
Further analysis of industrial enterprises in Nanhai District by industry shows that blue steel roofs may be more likely to be used in factories in industries that contribute more to GVIO and ICECADS. The top 10 (Figure 10
) of the GVIOADS by industry accounted for 73.35% of the total value. Some industries may occupy large areas, require large-scale equipment, and use colored steel roofs and steel frame plants. These include the smelting and pressing of nonferrous metals, the manufacture of electrical machinery and equipment, metal product industry, automobile industry, and the manufacture of nonmetallic mineral products. The top 10 of the ICECADS is shown in Figure 11
; they accounted for 91.04% of the total value. The industries that might use the colored steel roofs are production and the supply of electric power and heat power, the manufacture of nonmetallic mineral products, production and supply of electric power and heat power, metal product industry, automobile industry, manufacture of electrical machinery and equipment, and manufacture of raw chemical materials and chemical products.
To evaluate the performance of the deep learning method, the results were compared with the traditional maximum likelihood classification (MLC) method using the same remote sensing images. The comparison of the extraction results of the two methods is shown in Figure 12
. The DeeplabV3+ semantic segmentation model better extracted the boundary frame of the buildings, expressed the contour information of the building overall, and produced a more complete and detailed extraction with fewer misclassifications [54
]. When selecting samples for visual interpretation, the DeeplabV3+ method only needed to select blue steel roofs samples; in contrast, MLC needed to select both blue steel roofs and nonblue steel roofs training sample areas.
A comparative analysis of the four evaluation indexes revealed that the 10th region had the lowest values of the 20 regions among the verification samples: precision, 0.43; recall, 0.20; accuracy, 0.92; and F
1-score, 0.27. Based on other regions with high values, the low detection accuracy in the tenth region can be explained by the dimension of blue steel roofs and the shape, texture, and color of the surrounding buildings. The forecasting results and the distribution of blue steel sheds in the tenth area are shown in Figure 13
. The color change is what makes the model unable to distinguish the blue steel roofs from other buildings. Thus, the method used in this study still shows potential room for improvement for reducing the false detection rate.
In future research, further improvement of extraction accuracy could be achieved by adding samples and adjusting model parameters to ameliorate the low accuracy of small-scale targets and color recognition. To improve land-use efficiency and improve spatial quality, it would be useful to continue to use the model to learn more information about other features and extract data on various types of urban land use to elucidate the rate of urban land-use change and regional diversity. The experimental results could also be combined with meteorological data to develop ways of reducing the impact of the heat island effect.