Overcoming future challenges in spatial digital twin research

Links table

Abstract and 1 introduction

1.1. Spatial DVD (SDTS)

1.2. Applications

1.3. Various components of SDTS

1.4. The scope of this work and contributions

2. Related work and 2.1. Digital twins and variables

2.2. Spatial Digital Twin Studies

3. Building blocks of spatial digital twins and 3.1. Get and process data

3.2. Data modeling, storage and management

3.3. Huge data analysis system

3.4. Maps and intermediate programs based on geographic information systems

3.5. Main functional ingredients

4. Other related modern technologies and 4.1. Amnesty International and ML

4.2. Blockchain

4.3. Cloud computing

5. Challenges and future work, and 5.1. Acquire multi -defined and accurate data

5.2. NLP Spatial Information and 5.3. Measurement with databases and huge data platforms for SDT

5.4. Spatial visions and 5.5. Multi -media

5.6. Build an simulator environment

5.7. Imagine complex and varied interactions

5.8. Reducing security and privacy concerns

6. Conclusion and references

5.4. Automated spatial visions

Spatial digital twins generate huge amounts of data, often obtained from a wide range of assets. It is important to be able to automatically define exciting visions of this data without the need for human inputs. Moreover, techniques that can predict future behavior, risks, opportunities and trends are also important so that appropriate measures can be taken. While the visions that are determined automatically were studied [77, 78, 79]None of these technologies are specially designed for spatial data. Therefore, these technologies cannot provide decisive spatial visions to operate and manage spatial digital twins. Spatial visions can take different forms, such as the discovery of neighborhoods with abnormal greenhouse gas emissions, the detection of spatial connections between different features such as air quality and traffic accidents in different parts of the city, or maps of cars, highlighting areas with models that exceed their average and associated with the features that apparently get rid of them. Moreover, the time aspects of spatial data should also be seen in creating visions. For example, it may be interesting to study how the spatial relationship develops between two or more features over time. Unfortunately, current techniques cannot be applied or easily extended to a spatial numerical twin due to their inability to consider spatial features. Moreover, it is important to design effective technologies so that visions can be created in time, allowing system operators to intervene immediately if necessary.

5.5. Multi -media

The integration of the image and the text in multimedia models, such as the clip [103]It enables them to learn the data space jointly and effectively to face the challenges related to multimedia data. Moreover, the great developments in the large multimedia gym, such as GPT-4 [99]It showed great potential in the field of multimedia. We imagine that it is possible to answer many queries and find interesting visions from the satellite image/pictured drone image (i.e. bitten spatial data) with spatial and non -spatial data. More specifically, combining different forms of data such as data in actual time (for example, traffic, energy consumption) collected by sensors, may be the images taken by satellite or drone, spatial biology features, and training a large multimedia model capable of generating useful visions.

5.6. Build an simulator environment

Since many factors such as social interaction, economic factors and human factors may not be possible to capture in SDTS, it is important to build a simulator environment, as these factors can be simulated so that their association with SDT data that has been captured can be evaluated. Future research should focus on how to build a realistic simulation environment specifically designed for SDTS. Various simulation programs such as Anylogic, OpenSTudio and SIMIO have been developed for applications such as transportation, logistics and manufacturing services. Since the SDT range and size is greatly different from the DTS system, there is a possible way to search on how to develop a platform for simulating the various factors that include SDT.

5.7. Imagine complex and varied interactions

SDT includes different forms of data such as infrastructure data (for example, 3D buildings, roads, etc.), sensor data (for example, energy/gas/water consumption, traffic, etc.), social media data (for example, Twitter, Instagram, etc.). There are a number of challenges to visualize data that involve the interaction of these complex data objects. For example, given that the perception of more than three dimensions is incomprehensible for typical users, it is difficult to combine different forms of data, for example, 3D building with the consumption of the building’s time chains’ energy on the map. Data can also be of higher dimensions and different types, the perception of the connections between these data groups through the spatial and temporal dimension needs more research. Also, how to add and visualize different layers of data associated with a specific location on the map or GIS program to monitor interesting visions and the related data.