Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modern Information Retrieval
Toward the semantic geospatial web
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
POESIA: An ontological workflow approach for composing Web services in agriculture
The VLDB Journal — The International Journal on Very Large Data Bases
WOODSS and the Web: annotating and reusing scientific workflows
ACM SIGMOD Record
YAWL: yet another workflow language
Information Systems
Building place ontologies for the semantic web:: issues and approaches
Proceedings of the 4th ACM workshop on Geographical information retrieval
Discovering geographic locations in web pages using urban addresses
Proceedings of the 4th ACM workshop on Geographical information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
International Journal of Remote Sensing
A genetic programming framework for content-based image retrieval
Pattern Recognition
Overcoming semantic heterogeneity in spatial data infrastructures
Computers & Geosciences
A framework for semantic annotation of geospatial data for agriculture
International Journal of Metadata, Semantics and Ontologies
Searching satellite imagery with integrated measures
Pattern Recognition
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
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Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate. Moreover, annotation requires multi-disciplinary collaboration of researchers, with access to heterogeneous and distributed data sources and scientific computations. This paper illustrates our solution to approach this problem by means of a case study in agriculture. It shows how our implementation of a framework to automate the annotation of geospatial data can be used to process real data from remote sensing images and other official Brazilian data sources.