![]() The information supplied to the research consultants is limited to raw feedback responses and excludes the provision of your name, contact details or other information that could directly identify you. The insights derived from the feedback by the research consultants assist the City of Melbourne with identifying particular patterns or areas for service improvement and the creation of better projects, plans and policies. Phoenix Facilitation Limited (ABN 74 098 731 830).Capire Consulting Group (ABN 52 125 105 660).Global Research securely stores the raw feedback responses on Microsoft Azure servers located in Melbourne and Sydney data centres. ![]() The following research consultants engaged by the City of Melbourne to assist with reviewing, interpreting and gaining insights into feedback submitted via Participate Melbourne: We may also disclose personal information to third parties, and you consent to us disclosing your personal information, for the following purposes: We also review the site to protect it from spam or trolls. For example, we check to ensure each user has a single email account. The Knowledge Bank excludes your name, contact details or other information that could directly identify you.īy monitoring this additional information that you provide, we are able to protect the integrity of the discussions from individuals and groups who may attempt to unduly influence the outcomes of a consultation process. The City of Melbourne can filter the responses by subject matter or suburb to inform future projects, gain insights into neighbourhood sentiment or key themes, and to ensure the provision of consistent service delivery across the organisation. The Knowledge Bank tags feedback responses based on their subject matter and the suburb from which they are received. Staff members will also add any verbal feedback you provide to them in their interactions with you to the Knowledge Bank where you have consented. The City of Melbourne will generate an internal Knowledge Bank as a central repository of all public and closed feedback responses which will be made available to all City of Melbourne staff members. Your survey responses when forwarded to the relevant Council work area will exclude your name, contact details or other information that could directly identify you. The feedback you submit will be forwarded in the first instance to the relevant City of Melbourne work area with responsibility for administering the subject matter or project to which your feedback relates. Each post will contain your username and any publicly available reports from consultations, and may include quotes from participants in forums, surveys and other consultation tools. for any other purposes that we indicate to you when we request your personal information.Ĭontent that you post on the site publicly will be able to be viewed by everyone who uses the site. ![]() inform policy decisions by the City of Melbourne.analyse use of the site to inform ongoing improvements and enhancements.attribute content that you post on the site to you.respond to queries that you have contacted us about.send you newsletters and information, but only when you have agreed to us sending you this information or when the information is necessary to assist with the administration of the site.identify when you sign into your account.administer the site and manage your account.We compare the results with ground truth profiles and our results show that the proposed method extracts building counts and construction profiles with an accuracy of 95%.We use the personal information about you to: ![]() In the first step, satellite image is passed to a deep learning model that predicts segmentation masks over the built-up area following which building construction profiles are generated by overlaying digital maps over these predicted masks. ![]() Consequently, in this paper we propose a framework to address this problem by merging built-up area segmentation with digital maps. Although several unique solutions have been presented for this problem, this task can become extremely difficult for partially obscured buildings with densely overlapping boundaries, such as those found in underdeveloped countries like Pakistan. One strategy to achieve this is to extract building footprints and track them in multi-temporal data as observed in SpaceNet’s Challenges. Estimating the spatio-temporal profile of a building’s construction using high-resolution satellite images is a critical problem since it can be utilized for a variety of data-driven urban initiatives. ![]()
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