# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 1 Introduction  
Geospatial data collection is a research priority in the field of spatial information. Geospatial data collection includes field data collection, remote sensing data collection, and geographic information science data conversion [1]. Among these methods, field data collection is an essential means of gathering ground-truth spatial information [2]. Field data collection plays an important role in many geospatial applications, including crop yield estimation [3], hydrologic studies [4], mobile health [5], township land resources surveying [6], and so on. The methods of collecting geographic information in the field have undergone many developments. A few decades ago, traditional field geographic information collection relied heavily on field personnel to record data on paper maps or paper forms manually [7]. The process is time-consuming, inefficient, error-prone, and complex [8; 9; 10]. As time went by, with advancements in mobile devices, computer sciences, and satellite navigation technologies, researchers developed mobile device-based geographic information collection platforms [11; 12; 13]. These platforms integrate electronic maps, GPS positioning, information collection, and information uploading, liberating field personnel from traditional surveying constraints [14; 15]. They have been applied in many fields, such as disaster management [16], landscape ecology [17], soil research [18], and forest survey [19]. When mobile data collection platforms first emerged, researchers focused on developing customized mobile data collection platforms to serve specific scenarios [20]. For instance, Thonolli was specifically developed for fishermen to acquire and upload fishing information [21], STATUS was developed for the construction industry [22], and theCMR App was primarily used to record the tracks of wild animals [23]. These customized platforms can enhance the efficiency of geographic information collection and are tightly coupled with their intended uses [7; 24]. However, they cannot be applied to new collection scenarios, resulting in users having to develop new software [25]. Customized platforms often have limitations such as low flexibility, weak architecture reusability, and increased enterprise costs [26]. Generic software is designed to support users in various application scenarios without modification in the source code, thereby reducing the problem of repeated development for different application scenarios [27]. Based on the idea of generic software, researchers began to design generic mobile collection platforms. The generic mobile collection platform developed for field data collection needs to support users who are collecting data in various field collection scenarios. Various application scenarios mean a generic mobile collection platform must support those working under diverse environmental conditions and collect structurally varied spatial data [28]. Based on the existing research, environmental conditions include different network environments, common operating platforms, various basemap formats, and so on [29; 30]. Popular mobile collection platforms like ArcGIS Field Map [31], QField [32], Open Data Kit (ODK) [33], and FAIMS [34] support common spatial databases and attribute data under various environmental conditions. For example, ArcGIS Field Map and QField allow users to configure structured, text, and multimedia data, offering visualization controls such as drop-down boxes and multiple-choice buttons [35]. However, there is a nested attribute data structure in common collection scenarios, such as in-house surveys, where a house contains multiple households, each with multiple phone numbers, and the number of users and phone numbers is variable [36]. Current field collection software lacks solutions for this nested data issue. In addition, studies indicate that the standard geographic information collection workflow includes task configuration, task assignment, data collection, data review, data storage, data upload, data visualization, and data analysis [37; 38].