A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach

• URL: http://arxiv.org/abs/2410.15961v1
• Date: Mon, 21 Oct 2024 12:47:36 GMT
• Title: A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach
• Authors: Mahir Shahriar Dhrubo, Samira Akter, Anwarul Bashir Shuaib, Md Toki Tahmid, Zahid Hasan, A. B. M. Alim Al Islam,
• Abstract summary: Current manual digitization methods are time-consuming and labor-intensive. Our study proposes a semi-automated approach to streamline the digitization process, saving both time and human resources.
• Score: 2.315458677488431
• License:
• Abstract: Efficient vectorization of hand-drawn cadastral maps, such as Mouza maps in Bangladesh, poses a significant challenge due to their complex structures. Current manual digitization methods are time-consuming and labor-intensive. Our study proposes a semi-automated approach to streamline the digitization process, saving both time and human resources. Our methodology focuses on separating the plot boundaries and plot identifiers and applying our digitization methodology to convert both of them into vectorized format. To accomplish full vectorization, Convolutional Neural Network (CNN) models are utilized for pre-processing and plot number detection along with our smoothing algorithms based on the diversity of vector maps. The CNN models are trained with our own labeled dataset, generated from the maps, and smoothing algorithms are introduced from the various observations of the map's vector formats. Further human intervention remains essential for precision. We have evaluated our methods on several maps and provided both quantitative and qualitative results with user study. The result demonstrates that our methodology outperforms the existing map digitization processes significantly.

Related papers

• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• Neural Semantic Surface Maps [52.61017226479506]
  We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement.
  arXiv  Detail & Related papers  (2023-09-09T16:21:56Z)
• Asynchronously Trained Distributed Topographic Maps [0.0]
  We present an algorithm that uses $N$ autonomous units to generate a feature map by distributed training. Unit autonomy is achieved by sparse interaction in time & space through the combination of a distributed search, and a cascade-driven weight updating scheme.
  arXiv  Detail & Related papers  (2023-01-20T01:15:56Z)
• Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
  Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations. We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions. Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
  arXiv  Detail & Related papers  (2022-06-17T09:40:14Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach [6.510061176722248]
  In many trajectory-based applications, it is necessary to map raw GPS trajectories onto road networks in digital maps. In this paper, we consider the map-matching task from the data perspective, proposing a deep learning-based map-matching model. We generate synthetic trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data.
  arXiv  Detail & Related papers  (2021-08-01T11:51:11Z)
• CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency [61.40511574314069]
  Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. We propose CAMERAS, a technique to compute high-fidelity backpropagation saliency maps without requiring any external priors.
  arXiv  Detail & Related papers  (2021-06-20T08:20:56Z)
• Bayesian graph convolutional neural networks via tempered MCMC [0.41998444721319217]
  Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks. More recently, there has been more attention to unstructured data that can be represented via graphs. These types of data are often found in health and medicine, social networks, and research data repositories.
  arXiv  Detail & Related papers  (2021-04-17T04:03:25Z)
• Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks [0.0]
  Road intersections data have been used across different geospatial applications and analysis. We employed the standard paradigm of using deep convolutional neural network for object detection task named region-based CNN. Also, compared to the majority of traditional computer vision algorithms RCNN provides more accurate extraction.
  arXiv  Detail & Related papers  (2020-07-14T23:51:15Z)
• Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence [31.840880075039944]
  We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Key to our method is a feature-extraction network that learns directly from raw shape geometry.
  arXiv  Detail & Related papers  (2020-03-31T15:20:17Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.