TriangleConv: A Deep Point Convolutional Network for Recognizing Building Shapes in Map Space

The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space.

The willingness of volunteers to report changes on topographic maps

Various possibilities for collecting volunteer-provided geographical information in geodesy make it possible to engage volunteers for different purposes. In this paper, a study of the willingness of volunteers to report changes on topographic maps based on an online survey is presented. The survey was answered by 653 Slovenian respondents who use various online or classic topographic maps in their free time or at work and are willing to report their knowledge of changes in space or errors in maps to the map-updating institution. The survey's main finding is that 56% of respondents would use any online application to report changes on maps, 38% of respondents would prefer to report changes via email, and only 4% of respondents would prefer to report changes by phone. We also analysed the potential use of different functionalities of a web application for collecting changes and found that the most important functionalities for volunteers are those that give the most in-depth feedback (i.e., that a contribution has been submitted, that it is being verified, that it has been considered, that it has been deleted). The willingness of potential volunteers to use the various proposed functionalities also frequently depends on their current involvement with social networking sites or in volunteer associations and on their age group.

Description of the female of Ctenodontina mochica Lamas, revalidation of Ctenodontina carrerai (Hull) stat. rev., and comments on the taxonomy and distribution of Peruvian species of Ctenodontina Enderlein (Diptera: Asilidae: Asilinae)

This work analyzes the status of Peruvian species of Ctenodontina Enderlein, provides the description of the hitherto unknown female of Ctenodontina mochica Lamas, and proposes the revalidation of Ctenodontina carrerai (Hull) stat. rev. (currently regarded as a junior synonym of Ctenodontina maya Carrera & d’Andretta). Additionally, some comments about taxonomy, diagnostic features of male terminalia and distribution of four Peruvian Ctenodontina species, including a modification to the existing key to known species and a map updating their distribution records are given. We report Pachitea Melichar (Hemiptera: Cicadellidae) as prey of Ctenodontina nairae Vieira.

A WEBGIS FRAMEWORK FOR SEMI-AUTOMATED GEODATABASE UPDATING ASSISTED BY DEEP LEARNING

The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information Systems (GIS) have facilitated the broad and frequent use of GI by people from any background, thanks to the accessibility and the simplicity of the platforms. On the other hand, the increased use of GI also yielded a great increment in the demand for GI in different application areas. Thus, new algorithms and platforms allowing human intervention are immensely required for semi-automatic GI extraction to increase the accuracy. By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved. Thus, volunteers with limited knowledge on GIS software can be supported to perform accurate processing and to make guided decisions. In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers. The platform includes vector drawing and editing capabilities and employs a spatial database management system to store the final maps. The system is flexible and can utilise various DL methods in the image segmentation.

INDOOR POSITIONING FOR SMART DEVICES BASED ON SENSOR FUSION WITH PARTICLE FILTER: LOCALIZATION AND MAP UPDATING

With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.