Evaluating AI-Driven Automated Map Digitization in QGIS
- URL: http://arxiv.org/abs/2504.18777v1
- Date: Sat, 26 Apr 2025 03:09:54 GMT
- Title: Evaluating AI-Driven Automated Map Digitization in QGIS
- Authors: Diana Febrita,
- Abstract summary: Deepness, or Deep Neural Remote Sensing, is an advanced AI-driven tool designed and integrated as a plugin in QGIS application.<n>This study analyses AI-generated digitization results from Google Earth imagery and compares them with digitized outputs from OpenStreetMap (OSM) to evaluate performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Map digitization is an important process that converts maps into digital formats that can be used for further analysis. This process typically requires a deep human involvement because of the need for interpretation and decision-making when translating complex features. With the advancement of artificial intelligence, there is an alternative to conducting map digitization with the help of machine learning techniques. Deepness, or Deep Neural Remote Sensing, is an advanced AI-driven tool designed and integrated as a plugin in QGIS application. This research focuses on assessing the effectiveness of Deepness in automated digitization. This study analyses AI-generated digitization results from Google Earth imagery and compares them with digitized outputs from OpenStreetMap (OSM) to evaluate performance.
Related papers
- A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach [2.315458677488431]
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.
arXiv Detail & Related papers (2024-10-21T12:47:36Z) - A Sanity Check for AI-generated Image Detection [49.08585395873425]
We propose AIDE (AI-generated Image DEtector with Hybrid Features) to detect AI-generated images.<n>AIDE achieves +3.5% and +4.6% improvements to state-of-the-art methods.
arXiv Detail & Related papers (2024-06-27T17:59:49Z) - Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
We propose a transformer-based deep homography estimation (DHE) network.
It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification.
Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
arXiv Detail & Related papers (2024-02-25T13:22:17Z) - Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud
Segmentation in the Geosciences [9.899276249773425]
We present Tinto, a benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping.
Tinto comprises two complementary sets 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data from the ground-truth.
We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping.
arXiv Detail & Related papers (2023-05-17T03:24:08Z) - Explainable GeoAI: Can saliency maps help interpret artificial
intelligence's learning process? An empirical study on natural feature
detection [4.52308938611108]
This paper compares popular saliency map generation techniques and their strengths and weaknesses in interpreting GeoAI and deep learning models' reasoning behaviors.
The experiments used two GeoAI-ready datasets to demonstrate the generalizability of the research findings.
arXiv Detail & Related papers (2023-03-16T21:37:29Z) - Deep Learning Computer Vision Algorithms for Real-time UAVs On-board
Camera Image Processing [77.34726150561087]
This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs.
All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
arXiv Detail & Related papers (2022-11-02T11:10:42Z) - Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep
Learning [77.34726150561087]
We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data.
We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
arXiv Detail & Related papers (2022-09-28T18:51:59Z) - Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from
Depth Maps [66.24554680709417]
Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications.
We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
arXiv Detail & Related papers (2022-07-06T08:52:12Z) - Feature Visualization within an Automated Design Assessment leveraging
Explainable Artificial Intelligence Methods [0.0]
Automated capability assessment, mainly leveraged by deep learning systems driven from 3D CAD data, have been presented.
Current assessment systems may be able to assess CAD data with regards to abstract features, but without any geometrical indicator about the reasons of the system's decision.
Within the NeuroCAD Project, xAI methods are used to identify geometrical features which are associated with a certain abstract feature.
arXiv Detail & Related papers (2022-01-28T13:31:42Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - 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)
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.