[ { "path": "table_paper/2407.00017v1.json", "table_id": "1", "section": "5", "all_context": [ "To convert between CityJSON and CityJSONSeq files (and vice-versa), we have developed the open-source software cjseq, which is available at https://github.com/cityjson/cjseq/ under a permissive open-source license.", "The command-line program handles the conversion not only of the geometries, but also of the materials, the textures, and the geometry templates that the dataset could contain.", "It includes three sub-commands: cat: CityJSON CityJSONSeq; collect: CityJSONSeq CityJSON; filter: to filter city objects in a CityJSONSeq, randomly or based on a bounding box.", "It should be observed that the conversion is an efficient process: the rather large dataset Helskinki from Table 1 , which contains more than \\qty77000 buildings and whose CityJSON file is \\qty572\\mega, takes only \\qty4.7sec to be converted to a CityJSONSeq file, and the reverse operation takes \\qty5.7sec (on a standard laptop).", "" ], "target_context_ids": [ 3 ], "selected_paragraphs": [ "[paragraph id = 3] It should be observed that the conversion is an efficient process: the rather large dataset Helskinki from Table 1 , which contains more than \\qty77000 buildings and whose CityJSON file is \\qty572\\mega, takes only \\qty4.7sec to be converted to a CityJSONSeq file, and the reverse operation takes \\qty5.7sec (on a standard laptop)." ], "table_html": "
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Table 1: The datasets used for the benchmark.
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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
datasetsize of filevertices
CityObjects\napp.\nCityJSONCityJSONSeq\ncompr.\ntotal\nlargest\n\nshared\n
3DBAG\n\\qty1110 bldgs\n\n\\qty6.7\\mega\n\n\\qty5.9\\mega\n12%0.1%
3DBV\n\\qty71634 misc\n\n\\qty378\\mega\n\n\\qty317\\mega\n16%21.0%
Helsinki\n\\qty77231 bldgs\n\n\\qty572\\mega\n\n\\qty412\\mega\n28%0.0%
Helsinki_tex\n\\qty77231 bldgs\ntex\n\\qty713\\mega\n\n\\qty644\\mega\n10%0.0%
Ingolstadt\n\\qty55 bldgs\n\n\\qty4.8\\mega\n\n\\qty3.8\\mega\n25%0.0%
Montréal\n\\qty294 bldgs\ntex\n\\qty5.4\\mega\n\n\\qty4.6\\mega\n15%2.0%
NYC\n\\qty23777 bldgs\n\n\\qty105\\mega\n\n\\qty95\\mega\n10%0.8%
Railway\n\\qty50 misc\ntex+mat\n\\qty4.3\\mega\n\n\\qty4.0\\mega\n8%0.4%
Rotterdam\n\\qty853 bldgs\ntex\n\\qty2.6\\mega\n\n\\qty2.7\\mega\n-4%20.0%
Vienna\n\\qty307 bldgs\n\n\\qty5.4\\mega\n\n\\qty4.8\\mega\n11%0.0%
Zürich\n\\qty52834 bldgs\n\n\\qty279\\mega\n\n\\qty247\\mega\n11%2.6%
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    appearance: ‘tex’ is textures stored; ‘mat’ is material stored

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    compression factor is

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    number of vertices in the largest feature of the stream

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    percentage of vertices that are used to represent different city objects

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", "perturb_sentence_id": 3, "output": { "perturbed_statement": "[paragraph id = 3] It should be observed that the conversion is an efficient process: the rather large dataset Helsinki from Table 1, which contains more than \\qty67000 buildings and whose CityJSON file is \\qty562\\mega, takes only \\qty14.7sec to be converted to a CityJSONSeq file, and the reverse operation takes \\qty15.7sec (on a standard laptop).", "perturbed_explanation": "Original Explanation: The statement highlights the efficiency of the conversion process between CityJSON and CityJSONSeq, noting specific dataset characteristics and timing measurements. Altered Explanation: However, the statement introduces inaccuracies: 1. The dataset 'Helsinki' originally contains over \\qty77000 buildings, not \\qty67000. 2. The size of the CityJSON file is \\qty572\\mega, not \\qty562\\mega. 3. The conversion times are \\qty4.7sec and \\qty5.7sec, not \\qty14.7sec and \\qty15.7sec. These discrepancies lead to an incorrect portrayal of the dataset and its conversion process." } } ]