@article{
 title = {A framework for the modelling of uncertainty between remote sensing and geographic information},
 type = {article},
 year = {2000},
 keywords = {GIS,GIS integration,data modelling,transformation description,uncertainty},
 volume = {55},
 id = {8c09d6f6-16ac-3891-867a-3d5f87cd9163},
 created = {2018-05-29T14:06:02.445Z},
 file_attached = {false},
 profile_id = {6d8d7993-9618-3f6c-983a-9f6761313797},
 group_id = {4f1d95d1-59ee-3ce8-85ce-055cfae2da74},
 last_modified = {2018-05-29T14:06:02.445Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {13551},
 source_type = {article},
 private_publication = {false},
 abstract = {This paper addresses the modelling of uncertainty in an integrated geographic information system (GIS), specifically focused on the fusion of activities between GIS and remote sensing. As data is abstracted from its \textquoteleftraw\textquoteright form to the higher representations used by GIS, it passes through a number of different conceptual data models via a series of transformations. Each model and each transformation process contributes to the overall uncertainty present within the data. The issues that this paper addresses are threefold. Firstly, a description of various models of geographic space is given in terms of the inherent uncertainty characteristics that apply; this is then worked into a simple formalism. Secondly, the various transformation processes that are used to form geographic classes or objects from image data are described, and their effects on the uncertainty properties of data are stated. Thirdly, using the formalism to describe the transformation processes, a framework for the propagation of uncertainty through an integrated GIS is derived. By way of a summary, a table describing sources of accumulated uncertainty across four underlying models of geographic space is derived.},
 bibtype = {article},
 author = {Gahegan, M and Ehlers, M},
 journal = {ISPRS Journal Of Photogrammetry And Remote Sensing}
}

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