Volunteer classifications of images from the Cropland Capture game

<p>Each entry represents a single classification of a single image by a volunteer rater.</p> <p>The dataset contains six columns:</p> <p>imgid: &nbsp;&nbsp; &nbsp;The unique identifier for each image used in the Cropland Capture campaign<br> userid:&nbsp;&nbsp; &nbsp;The unique identifier for each volunteer in the Cropland Capture campaign<br> rating:&nbsp;&nbsp; &nbsp;The answer provided; can be only one of the following:<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1: yes cropland<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;2: no cropland<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;0: maybe&nbsp;<br> date:&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Timestamp of the rating<br> ratingid:&nbsp;&nbsp; &nbsp;The unique identifier of the rating (this is different for each data row)<br> platform:&nbsp;&nbsp; &nbsp;What interface did the volunteer use to provide this rating?<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1: iPhone5<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;2: iPhone, other models<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;3: iPad<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;4: Browser<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &gt;100: Android; different numbers indicate the screen size in pixels&nbsp;</p> <p>For more information, please see the following publications:</p> <p>Salk, CF, T Sturn, L See, S Fritz (2017).&nbsp; Limitations of majority agreement in crowdsourced image interpretation. <em>Transactions in GIS</em>, 21: 207&ndash;223.</p> <p>Salk, CF, T Sturn, L See, S&nbsp; Fritz (2016).&nbsp; Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task.&nbsp; <em>Remote Sensing</em>, 8: 744.</p> <p>Salk, CF, T Sturn, L See, S Fritz and C Perger (2016).&nbsp; Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game.&nbsp; <em>International Journal of Digital Earth</em>, 9(4): 410-426.</p>