Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

Air pollution prediction with multi-modal data and deep neural networks

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem

다중 센싱 플랫폼과 딥러닝을 활용한 도시 규모의 수목 맵핑 및 수종 탐지

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2023. 2. 류영렬.Precise estimation of the number of trees and individual tree location with species information all over the city forms solid foundation for enhancing ecosystem service. However, mapping individual trees at the city scale remains challenging due to heterogeneous patterns of urban tree distribution. Here, we present a novel framework for merging multiple sensing platforms with leveraging various deep neural networks to produce a fine-grained urban tree map. We performed mapping trees and detecting species by relying only on RGB images taken by multiple sensing platforms such as airborne, citizens and vehicles, which fueled six deep learning models. We divided the entire process into three steps, since each platform has its own strengths. First, we produced individual tree location maps by converting the central points of the bounding boxes into actual coordinates from airborne imagery. Since many trees were obscured by the shadows of the buildings, we applied Generative Adversarial Network (GAN) to delineate hidden trees from the airborne images. Second, we selected tree bark photos collected by citizen for species mapping in urban parks and forests. Species information of all tree bark photos were automatically classified after non-tree parts of images were segmented. Third, we classified species of roadside trees by using a camera mounted on a car to augment our species mapping framework with street-level tree data. We estimated the distance from a car to street trees from the number of lanes detected from the images. Finally, we assessed our results by comparing it with Light Detection and Ranging (LiDAR), GPS and field data. We estimated over 1.2 million trees existed in the city of 121.04 km² and generated more accurate individual tree positions, outperforming the conventional field survey methods. Among them, we detected the species of more than 63,000 trees. The most frequently detected species was Prunus yedoensis (21.43 %) followed by Ginkgo biloba (19.44 %), Zelkova serrata (18.68 %), Pinus densiflora (7.55 %) and Metasequoia glyptostroboides (5.97 %). Comprehensive experimental results demonstrate that tree bark photos and street-level imagery taken by citizens and vehicles are conducive to delivering accurate and quantitative information on the distribution of urban tree species.도시 전역에 존재하는 모든 수목의 숫자와 개별 위치, 그리고 수종 분포를 정확하게 파악하는 것은 생태계 서비스를 향상시키기 위한 필수조건이다. 하지만, 도시에서는 수목의 분포가 매우 복잡하기 때문에 개별 수목을 맵핑하는 것은 어려웠다. 본 연구에서는, 여러가지 센싱 플랫폼을 융합함과 동시에 다양한 딥러닝 네트워크들을 활용하여 세밀한 도시 수목 지도를 제작하는 새로운 프레임워크를 제안한다. 우리는 오직 항공사진, 시민, 차량 등의 플랫폼으로부터 수집된 RGB 이미지만을 사용하였으며, 6가지 딥러닝 모델을 활용하여 수목을 맵핑하고 수종을 탐지하였다. 각각의 플랫폼은 저마다의 강점이 있기 때문에 전 과정을 세 가지 스텝으로 구분할 수 있다. 첫째, 우리는 항공사진 상에서 탐지된 수목의 딥러닝 바운딩 박스로부터 중심점을 추출하여 개별 수목의 위치 지도를 제작하였다. 많은 수목이 도시 내 고층 빌딩의 그림자에 의해 가려졌기 때문에, 우리는 생정적 적대적 신경망 (Generative Adversarial Network, GAN)을 통해 항공사진 상에 숨겨진 수목을 그려내고자 하였다. 둘째, 우리는 시민들이 수집한 수목의 수피 사진을 활용하여 도시 공원 및 도시 숲 일대에 수종 정보를 맵핑하였다. 수피 사진으로부터의 수종 정보는 딥러닝 네트워크에 의해 자동으로 분류되었으며, 이 과정에서 이미지 분할 모델 또한 적용되어 딥러닝 분류 모델이 오로지 수피 부분에만 집중할 수 있도록 하였다. 셋째, 우리는 차량에 탑재된 카메라를 활용하여 도로변 가로수의 수종을 탐지하였다. 이 과정에서 차량으로부터 가로수까지의 거리 정보가 필요하였는데, 우리는 이미지 상의 차선 개수로부터 거리를 추정하였다. 마지막으로, 본 연구 결과는 라이다 (Light Detection and Ranging, LiDAR)와 GPS 장비, 그리고 현장 자료에 의해 평가되었다. 우리는 121.04 km² 면적의 대상지 내에 약 130만여 그루의 수목이 존재하는 것을 확인하였으며, 다양한 선행연구보다 높은 정확도의 개별 수목 위치 지도를 제작하였다. 탐지된 모든 수목 중 약 6만 3천여 그루의 수종 정보가 탐지되었으며, 이중 가장 빈번히 탐지된 수목은 왕벚나무 (Prunus yedoensis, 21.43 %)였다. 은행나무 (Ginkgo biloba, 19.44 %), 느티나무 (Zelkova serrata, 18.68 %), 소나무 (Pinus densiflora, 7.55 %), 그리고 메타세쿼이어 (Metasequoia glyptostroboides, 5.97 %) 등이 그 뒤를 이었다. 포괄적인 검증이 수행되었고, 본 연구에서는 시민이 수집한 수피 사진과 차량으로부터 수집된 도로변 이미지는 도시 수종 분포에 대한 정확하고 정량적인 정보를 제공한다는 것을 검증하였다.1. Introduction 6 2. Methodology 9 2.1. Data collection 9 2.2. Deep learning overall 12 2.3. Tree counting and mapping 15 2.4. Tree species detection 16 2.5. Evaluation 21 3. Results 22 3.1. Evaluation of deep learning performance 22 3.2. Tree counting and mapping 23 3.3. Tree species detection 27 4. Discussion 30 4.1. Multiple sensing platforms for urban areas 30 4.2. Potential of citizen and vehicle sensors 34 4.3. Implications 48 5. Conclusion 51 Bibliography 52 Abstract in Korean 61석

GeoAI in Social Science

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps. It also discusses the importance of breaking down data silos, accelerating convergence among GeoAI research methods, as well as moving GeoAI beyond geospatial benefits.Comment: Artificial Intelligence; social science; deep learning; convergence; knowledge grap

Flexible-Position MIMO for Wireless Communications: Fundamentals, Challenges, and Future Directions

The flexible-position multiple-input multiple-output (FLP-MIMO), such as fluid antennas and movable antennas, is a promising technology for future wireless communications. This is due to the fact that the positions of antennas at the transceiver and reflector can be dynamically optimized to achieve better channel conditions and, as such, can provide high spectral efficiency (SE) and energy efficiency (EE) gains with fewer antennas. In this article, we introduce the fundamentals of FLP-MIMO systems, including hardware design, structure design, and potential applications. We shall demonstrate that FLP-MIMO, using fewer flexible antennas, can match the channel hardening achieved by a large number of fixed antennas. We will then analyze the SE-EE relationship for FLP-MIMO and fixed-position MIMO. Furthermore, we will design the optimal trajectory of flexible antennas to maximize system sum SE or total EE at a fixed travel distance of each antenna. Finally, several important research directions regarding FLP-MIMO communications are presented to facilitate further investigation.Comment: 10 pages, 5 figures, 1 tables, accepted by IEEE Wireless Communications Magazin

Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces challenges such as a lack of clear definitions, systematic reviews, and universalizable solutions. To this end, this paper first introduces the concept of UFM and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes current UFM-related works, based on urban data modalities and types. Furthermore, to foster advancement in this field, we present a promising framework aimed at the prospective realization of UFMs, designed to overcome the identified challenges. Additionally, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at https://github.com/usail-hkust/Awesome-Urban-Foundation-Models

State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods

Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replication, hindering the definition of the state-of-art. Accordingly, aim of this scoping review was to analyze the latest scientific research on air-quality modelling, focusing on particulate matter, identifying the most widespread solutions and trying to compare them. The review was mainly focused, but not limited to, machine learning applications. An initial set of 940 results published in 2022 were returned by search engines, 142 of which resulted significant and were analyzed. Three main modelling scopes were identified: correlation analysis, interpolation and forecast. Most of the studies were relevant to east and southeast Asia. The majority of models were multivariate, including (besides ground stations) meteorological information, satellite data, land use and/or topography, and more. 232 different algorithms were tested across studies (either as single-blocks or within ensemble architectures), of which only 60 were tested more than once. A performance comparison showed stronger evidence towards the use of Random Forest modelling, in particular when included in ensemble architectures. However, it must be noticed that results varied significantly according to the experimental set-up, indicating that no overall best solution can be identified, and a case-specific assessment is necessary

Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain

Wheeled robot navigation has been widely used in urban environments, but little research has been conducted on its navigation in wild vegetation. External sensors (LiDAR, camera etc.) are often used to construct point cloud map of the surrounding environment, however, the supporting rigid ground used for travelling cannot be detected due to the occlusion of vegetation. This often causes unsafe or not smooth path during planning process. To address the drawback, we propose the PE-RRT* algorithm, which effectively combines a novel support plane estimation method and sampling algorithm to generate real-time feasible and safe path in vegetation environments. In order to accurately estimate the support plane, we combine external perception and proprioception, and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the terrain at the sampling nodes. We build a physical experimental platform and conduct experiments in different outdoor environments. Experimental results show that our method has high safety, robustness and generalization