Cataloging Public Objects Using Aerial and Street-Level Images – Urban Trees

Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-of-the-art CNN-based object detectors and classifiers. We test our method on “Pasadena Urban Trees”, a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance

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

Archeological Survey For The Temple-Belton Regional Sewer System Improvement Project, Bell County, Texas

Prewitt and Associates, Inc., (PAI) was contracted by Kasberg, Patrick, and Associates to perform an intensive archeological survey prior to the proposed installation of new sewer lines, the expansion of one lift station, and the construction of another lift station in Bell County,Texas.This investigation was conducted in April 2013 in compliance with the Texas Antiquities Code.The Temple-Belton Regional Sewer System (TBRSS) Improvement Project will construct a new 1.7-mile-long (8,730-ft-long) Shallowford force Main sewer line from the Temple-Belton wastewater Treatment Plant on fM 93 to the Shallowford Lift Station just north of the Leon River. The project also calls for small expansions of the Shallowford Lift Station and Belton Lift Station. The survey recorded two new archeological sites, 41BL1380 and 41BL1381, and revisited one previously recorded site, 41BL260. All three sites are recommended as not eligible for listing in the National Register or for designation as State Antiquities Landmarks. The TBRSS project was put on hold in 2013 and 2014, and PAI archeologists did not conduct trenching in a 2,788-ft-long section of the force main alignment where the landowner denied right of entry. This segment in the Leon River valley has the potential to contain intact buried archeological remains in holocene-age alluvium. Consequently, this section of the force main alignment will need to be investigated with mechanical trenching if the TBRSS project is resurrected

Geocoding of trees from street addresses and street-level images

We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales

Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments, forcing them to deal with a constant threat to their palm trees. Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage, and is most commonly performed by local physical access for individual tree monitoring. Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms, with aerial and street-level imagery data. To detect infested palm trees we analyzed over 100,000 aerial and street-images, mapping the location of palm trees in urban areas. Using this procedure, we discovered and verified infested palm trees at various locations