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Road extraction is a sub-domain of remote sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. Convolutional neural networks (CNNs), especially the DeepLab series known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects’ features, address some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in the place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP with a CNN backbone network and a Squeeze-and-Excitation block will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from remote sensing images. The Results Section presents a comparison of our model’s performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach.
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SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery Behavior
Accurate road networks play a crucial role in modern mobile applications such as navigation and last-mile delivery. Most existing studies primarily focus on generating road networks in open areas like main roads and avenues, but little attention has been given to the generation of community road networks in closed areas such as residential areas, which becomes more and more significant due to the growing demand for door-to-door services such as food delivery. This lack of research is primarily attributed to challenges related to sensing data availability and quality. In this paper, we design a novel framework called SmallMap that leverages ubiquitous multi-modal sensing data from last-mile delivery to automatically generate community road networks with low costs. Our SmallMap consists of two key modules: (1) a Trajectory of Interest Detection module enhanced by exploiting multi-modal sensing data collected from the delivery process; and (2) a Dual Spatio-temporal Generative Adversarial Network module that incorporates Trajectory of Interest by unsupervised road network adaptation to generate road networks automatically. To evaluate the effectiveness of SmallMap, we utilize a two-month dataset from one of the largest logistics companies in China. The extensive evaluation results demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving a precision of 90.5%, a recall of 87.5%, and an F1-score of 88.9%, respectively. Moreover, we conduct three case studies in Beijing City for courier workload estimation, Estimated Time of Arrival (ETA) in last-mile delivery, and fine-grained order assignment.
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- Award ID(s):
- 2047822
- PAR ID:
- 10538835
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 8
- Issue:
- 2
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3659596
