The Kilombero catchment in Tanzania is an important area of recent development in East Africa. It harbors a large floodplain wetland, put under protection and designated a Ramsar site. Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In the underlying study, we assessed LULC changes of the Kilombero catchment in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics of Landsat data that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show a) only PCC changes, b) only spectral changes that do not affect the classification of a pixel, c) both types of change, or d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. Half of the Kilombero floodplain was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management.This dataset contains LULC maps for 1974, 1994, 2004, and 2014 and class-specific per-pixel classification probabilities for each map. We also provide the data points used for the study. These data points were collected during field campaigns and complemented by interpreted high-spatial resolution Google Earth images following a systematic random sampling. For appropriate map display we provide QGIS style files associated with the outputs.This research was conducted at the University of Bonn, Germany, within the GlobE - Wetlands in East Africa project, which was funded by the German Federal Ministry of Education and Research (FKZ: 031A250 A-H) with additional funding provided by the German Federal Ministry of Economic Cooperation and Development.