The aerial photo flight index shows the aerial photo and flight information, including photo number, shooting position, photo coverage, date of flight, flying height etc., of the aerial photographs taken by Survey and Mapping Office (SMO). The index is geo-referenced to Hong Kong 1980 Grid System and in FGDB format. It is a set of data made available by the Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

The aerial photo flight index shows the aerial photo and flight information, including photo number, shooting position, photo coverage, date of flight, flying height etc., of the aerial photographs taken by Survey and Mapping Office (SMO). There are three file formats, XLS / XLSX, KML and KMZ for download. The file in KML and KMZ file format is geo-referenced to World Geodetic System (WGS84), containing both aerial photo and flight information, while the file in XLS / XLSX file format only contains the aerial photo information.

This web map features a detailed vector reference layer for the world that is overlaid on World Imagery. The web map is similar in content and style to the popular Imagery with Labels map, which uses layers with raster fused map cache. This map includes a vector tile layer that provides unique capabilities for customization and high-resolution display. This reference map uses a vector tile layer that includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. This vector tile layer is built using the same data sources used for other Esri basemaps. The World Imagery layer in this map provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide.Use this Map This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map. Customize this Map Because this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector web maps. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.

Dataset of the publication - "Remotely Sensed Environmental Data as Ecological Proxies for Ground-Dwelling Ant Diversity along a Subtropical Forest Succession Gradient". The dataset contains three CSV files: a species community data, species trait data, and remotely sensed variables. For the species community data, ground-dwelling ant fauna was collected using pitfall traps along the vegetation successional gradient in Hong Kong SAR. A total of 80 sites, including 15 grasslands, 21 shrublands, and 44 secondary forests, were sampled from 2015 to 2017. A total of 141 species, belonging to 55 genera, were recorded. For the species trait data, we measured body size and six other commonly used ecological traits, including eye length, leg length, head width, antennae length, clypeus length, and mandible length, to estimate the multidimensional trait space of ant communities along the forest successional gradient. An average of five randomly selected worker individuals were measured per species (mean ± SD: 4.83 ± 1.28). Lastly, for remotely sensed variables, vegetation structural metrics and topographic metrics were derived from airborne LiDAR data collected in 2010, as well as anthropogenic fire history detected from aerial photos. Vegetation structural metrics include canopy cover, mean vegetation height, vertical distribution ratio, understory cover, mean understory height, coefficient of variation of canopy height, and standard deviation of vertical distribution ratio. Topographic metrics include elevation, insolation, and topographic wetness index. For details about the method of data collection, please refer to the publication.

Planning Department (PlanD) has prepared a 3D photo-realistic model for part of Hong Kong Island and Kowloon Peninsula. The 3D photo-realistic model for Hong Kong Island (part) and Kowloon Peninsula (part) were formulated based on the aerial photos captured in March 2017 and March 2018 respectively. The index plan and the content of CSV file can be found here Metadata File for Kowloon Peninsula Data can be found here The multiple file formats are available for dataset download in API. DISCLAIMER The data provided by the PlanD is for reference only. Whilst endeavours have been made to ensure the accuracy of the data on this site, no express or implied warranty or representation is given to the accuracy or completeness of the data or its appropriateness for use in any particular circumstances. The PlanD is not responsible for any loss or damage whatsoever arising out of or in connection with this web site and if necessary you should obtain independent legal advice before acting upon it. The PlanD reserves the right to omit, suspend or edit all data at any time in its absolute discretion without giving any reason or prior notice. Users are responsible for making their own assessment of all data. The PlanD shall not be held liable for any damages whatsoever (including, without limitation, damages for loss of profits, business interruption, loss of information) arising out of the use or inability to use such data. The PlanD does not undertake to provide any updated version of the data and reserves the right to suspend the provision of the data at any time. The time required for downloading the data would depend on the current network environment.

Context

This dataset was originally published by the University of Zurich Robotics and Perception Group here. A sample of the data along with accompanying descriptions is provided here for research uses.

This presents the world's first dataset recorded on-board a camera equipped Micro Aerial Vehicle (MAV) flying within urban streets at low altitudes (i.e., 5-15 meters above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, GPS and IMU sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based topological localization, monocular visual odometry, simultaneous localization and mapping (SLAM), and online 3D reconstruction algorithms for MAV in urban environments.

Content

The entire dataset is roughly 28 gigabyte. We also provide a sample subset less than 200 megabyte, representing the first part of the dataset. You can download the entire dataset from this page.

The dataset contains time-synchronized high-resolution images (1920 x 1080 x 24 bits), GPS, IMU, and ground level Google-Street-View images. The high-resolution aerial images were captured with a rolling shutter GoPro Hero 4 camera that records each image frame line by line, from top to bottom with a readout time of 30 millisecond. A summary of the enclosed files is given below.

The data from the on-board barometric pressure sensor BarometricPressure.csv, accelerometer RawAccel.csv, gyroscope RawGyro.csv, GPS receiver OnbordGPS.csv, and pose estimation OnboardPose.csv is logged and timesynchronized using the clock of the PX4 autopilot board. The on-board sensor data was spatially and temporally aligned with the aerial images. The first column of every file contains the timestamp when the data was recorded expressed in microseconds. In the next columns the sensor readings are stored. The second column in OnbordGPS.csv encodes the identification number (ID) of every aerial image stored in the /MAV Images/ folder. The first column in GroundTruthAGL.csv is the ID of the aerial image, followed by the ground truth camera position of the MAV and the raw GPS data. The second column in GroundTruthAGM.csv is the ID of of the aerial image, followed by the ID of the first, second and third best match ground-level street view image in the /Street View Img/ folder.

Ground Truth

Two types of ground truth data are provided in order to evaluate and benchmark different vision-based localization algorithms. Firstly, appearance-based topological localization algorithms, that match aerial images to street level ones, can be evaluated in terms of precision rate and recall rate. Secondly, metric localization algorithms, that computed the ego-motion of the MAV using monocular visual SLAM tools, can be evaluated in terms of standard deviations from the ground truth path of the vehicle.

See more details here.

Past Research

The work listed below inspired the recording of this dataset. In these papers a much smaller dataset was used, that did not contain time synchronized GPS (except a small street segment ), IMU data and accurate metric ground truth. If you used this dataset, please send your paper to majdik (at) ifi (dot) uzh (dot) ch.

A.L. Majdik, D. Verda, Y. Albers-Schoenberg, D. Scaramuzza. Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles Journal of Field Robotics, 2015.

A. L. Majdik, D. Verda, Y. Albers-Schoenberg, D. Scaramuzza Micro Air Vehicle Localization and Position Tracking from Textured 3D Cadastral Models IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014.

A. Majdik, Y. Albers-Schoenberg, D. Scaramuzza. MAV Urban Localization from Google Street View Data IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, 2013.

License

These datasets are released under the Creative Commons license (CC BY-NC-SA 3.0), which is free for non-commercial use (including research).

Acknowledgements

This dataset was recorded with the help of Karl Schwabe, Mathieu Noirot-Cosson, and Yves Albers-Schoenberg. To record the dataset we used a Fotokite MAV offered to our disposal by Perspective Robotics AG—http://fotokite.com.

This work was supported by the National Centre of Competence in Research Robotics (NCCR) through the Swiss National Science Foundation and by the Hungarian Scientific Research Fund (No. OTKA/NKFIH 120499).