Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, 12 the occurrence of uncertainties in producing geographic data is inevitable. However, most studies 13 concentrated solely on assessing the classification accuracy and neglecting the inherent 14 uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA15 based slum detection. We selected Jakarta as our case study area, because of a national policy of 16 slum eradication, which is causing rapid changes in slum areas. Our research comprises of four 17 parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty 18 measurements. Existential and extensional uncertainty arise when producing reference data. The 19 comparison of a manual expert delineations of slums with OBIA slum classification results into four 20 combinations: True Positive, False Positive, True Negative and False Negative. However, the higher 21 the True Positive (which lead to a better accuracy), the lower the certainty of the results. This 22 demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non23 observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where 24 uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher 25 classification accuracy by matching manual delineation and OBIA classification. 26