Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

Assessment of street forest characteristics in four African cities using google street view measurement: Potentials and implications

Accurate information on urban forests of tree sizes, health state, community structures, and spatial distribution is still limited in African cities. Using a Google Street View (GSV)-based tree-size measuring method developed by our team, this paper aims to evaluate street trees of four African metropolitan cities using GSV data. The study compiled a large dataset with 46,016 street trees in 3454 sites in Kampala, Nairobi, Bloemfontein, and Johannesburg. The data including tree size (diameter at breast height, DBH; tree height, TH; underbranch height, UBH; canopy size), tree floristic composition (apical dominance types, broadleaf-conifer-palm leaf, flowering or not), tree health (leaf color, diebacks, dead tree, and bracket-supporting percent), streetside development (lane number, roadside shops, parking vehicle, and pedestrian density), and geolocation (latitude, longitude). These data can be spatially visualized with the help of ArcGIS, and the large dataset favors reliable maps from the street-view level. Data statistics showed that four cities were dominated by broad-leaved, apical dominance, and flowering trees, with a low level of unhealthy leaves and a tiny percentage of dead. The arbor-shrubs-herb structure vegetation dominated all four cities. Kampala had the most slender trees (DBH = 23 cm, TH = 8.4 m), while Nairobi and Johannesburg had the thickest trees (DBH = 38 cm, TH = 8.5-8.6 m). Bare land rates were lowest at 23% in Bloemfontein and highest at 33% in Nairobi. Principal analysis and Pearson correlations showed that these tree variations were closely associated with street development and local land use configuration. By comparing the urban tree data in other regions of the world, we found that the trees in African cities are generally giant but have a lower density (the trees within a 100-m street segment). Our findings emphasized that GSV data is feasible enough for urban forest monitoring in Africa, and the database is helpful for urban landscape planning and management.

A deep semantic vegetation health monitoring platform for citizen science imaging data

Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation's colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015-2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices.

Automatic Positioning of Street Objects Based on Self-Adaptive Constrained Line of Bearing from Street-View Images

In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes.