give the key information about the situation like name, price, date, area, and times.
People can get these reports from various sources like website, smartphone apps,
newspapers, magazines, TV, radio, the analysis reports of nonprofit institutes, stock
market reports, etc. The cost depends on the terms of data sources. Many of them are
freely available.
4.2.5 Summary
Each source has its pros and cons. The capabilities of platforms may be partially but
never totally overlapped. Thus these sensors are not diametrically opposed to each
other. Nevertheless, “data are never enough.” The four categories, and maybe more
in the future, of sensors, can collaborate and sense the agricultural entities from
various spatial and temporal perspectives to provide an immersive reflection of the
truth. Integrative use of the available databases of the different platforms in the same
application use case is definitely an inevitable direction. Actually, the game has
already begun.
4.3 Sourcing
This section surveys the sourcing methods for agro-geoinformatics. Data sourcing
means finding the sources of the right data and sweeping away all the obstacles to get
it. There are two tasks: exploration and retrieval. Too many incredible issues
happened surrounding such two things in the real-world cases. We first introduce
the conventional sourcing method and then address the newest technique: cloudbased sourcing and crowdsourcing.
4.3.1 Conventional Sourcing
In the traditional way, data users have to frequently perform the following steps:
1. Find available sources.
2. Ask each source to provide sample data.
3. Check if the structure, resolution, quality, and fields of the gained samples meet
the use requirements.
4. Figure out how the data are going to be used.
5. Understand the observation date and acquisition date of the data.
6. Obtain the data from the databases if they are directly accesible online.
7. Contact the eligible sources to purchase the data if it is not freely available online.
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People can get these reports from various sources like website, smartphone apps,
newspapers, magazines, TV, radio, the analysis reports of nonprofit institutes, stock
market reports, etc. The cost depends on the terms of data sources. Many of them are
freely available.
4.2.5 Summary
Each source has its pros and cons. The capabilities of platforms may be partially but
never totally overlapped. Thus these sensors are not diametrically opposed to each
other. Nevertheless, “data are never enough.” The four categories, and maybe more
in the future, of sensors, can collaborate and sense the agricultural entities from
various spatial and temporal perspectives to provide an immersive reflection of the
truth. Integrative use of the available databases of the different platforms in the same
application use case is definitely an inevitable direction. Actually, the game has
already begun.
4.3 Sourcing
This section surveys the sourcing methods for agro-geoinformatics. Data sourcing
means finding the sources of the right data and sweeping away all the obstacles to get
it. There are two tasks: exploration and retrieval. Too many incredible issues
happened surrounding such two things in the real-world cases. We first introduce
the conventional sourcing method and then address the newest technique: cloudbased sourcing and crowdsourcing.
4.3.1 Conventional Sourcing
In the traditional way, data users have to frequently perform the following steps:
1. Find available sources.
2. Ask each source to provide sample data.
3. Check if the structure, resolution, quality, and fields of the gained samples meet
the use requirements.
4. Figure out how the data are going to be used.
5. Understand the observation date and acquisition date of the data.
6. Obtain the data from the databases if they are directly accesible online.
7. Contact the eligible sources to purchase the data if it is not freely available online.
56
Z. Sun et al.
56