Integrating Aerial and Street View Images for Urban Land Use Classification

Cao, Rui; Zhu, Jiasong; Tu, Wei; Li, Qingquan; Cao, Jinzhou; Liu, Bozhi; Zhang, Qian; Qiu, Guoping

doi:10.3390/rs10101553

Cited by 127 publications

(92 citation statements)

References 55 publications

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“…Nevertheless, the OBIA approach as applied in the present study relies on a labor-intensive survey, which needs expertise in image converting. This is generally time consuming and expensive [143,144]. Furthermore, mistakes due to human operation are possible [145].…”

Section: Discussionmentioning

confidence: 99%

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

Carl

Lehmann²,

Landgraf

et al. 2019

Forests

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Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology-object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97% and 99.5% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust.Forests 2019, 10, 235 2 of 23 energy policies aimed towards a reduction in the use of fossil fuels and greater utilization of renewable energy since 2004 [7] marked an increase in fast-growing tree production, including black locust in short rotation coppices (harvested every 2-20 years) [8,9]. Nevertheless, some studies have declared black locust to be an invasive non-native tree species in Europe [6,[10][11][12]. However, other studies consider black locust to be an alternative tree species, for example for European ash (Fraxinus excelsior) [13,14], whose high dieback is caused by the fungal pathogen Hymenoscyphus fraxineus [14].Black locust possesses many growth characteristics that makes it ideal for short-rotation biomass production such as: rapid growth, high drought tolerance, and nitrogen-fixation [15,16], as well as the ability to reproduce via stump shoots in response to harvest. Black locust starts flowering at the age of six years [17]. Many insects benefit from this characteristic, including honeybees [18]. Moreover, the production of black locust honey is very common and economically important, especially in Hungary [17]. A maximum of 15,559 flowers per tree were counted in an eight-year-old black locust plantation [18], and 12,000 seeds/m 2 were identified in a monodominant stand [6,19]; black locust thus produces an abundance of seeds. As black locust belongs to the Fabaceae family, the seed coat is hard and impermeable [20,21]. Hence, seeds require scarification for successful germination [21][22][23] and priming of seeds is favorable [21,24,25]. Seeds pre...

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Section: Discussionmentioning

confidence: 99%

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

Carl

Lehmann²,

Landgraf

et al. 2019

Forests

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“…Lefèvre et al (2017) detects changes by with siamese CNNs through comparing aerial imagery and street view panoramas warped to aerial image geometry. Cao et al (2018) classify land use categories in urban areas with semantic features from both aerial and ground-level images. geo-localize traffic lights and telegraph poles in street-level images with monocular depth estimation using CNNs.…”

Section: Related Workmentioning

confidence: 99%

Geocoding of trees from street addresses and street-level images

Laumer

Lang

Doorn

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for > 50000 trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38 % of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale.

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“…Furthermore, researchers used street view imagery in congruence with satellite imagery to enhance the process of digitizing maps. Cao et al used an integrative approach of using satellite imagery and street view to infer land use then classify buildings and points of interest (POIs) [6].…”

Section: Literaturementioning

confidence: 99%

Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

Alhasoun

González

2019

2019 IEEE International Conference on Big Data (Big Data)

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The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined with the transportational features of a street. In such classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning has been adopted by major cities such as the city of San Francisco, the states of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning. We collect and label street imagery then train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87%, we then visualize samples from the embedding space of streets using the t-SNE method and apply class activation mapping methods to interpret the features in street imagery contributing to output classification from a model.

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Integrating Aerial and Street View Images for Urban Land Use Classification

Cited by 127 publications

References 55 publications

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis

Geocoding of trees from street addresses and street-level images

Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

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