Land-use planners, climate researchers, and policymakers require a transparent land-use and land-cover (LULC) change modeling software, supporting various aspects of iterative modeling life cycle through integrated but loosely coupled modules built using appropriate techniques into a single platform. With this premise, an 'Open-source Land-use and Land-cover Dynamics Modeling platform' (OpenLDM) is developed and presented in this paper. The platform integrates different components of empirical land change modeling cycle such as model selection from the available parametric and nonparametric methods; suitability map generation; demand generation; allocation (in spatial or non-spatial context); simulation of future LULC map based on business-as-usual or demand-and-policy-driven scenarios; accuracy assessment; and visualization. Suitability maps for each LULC class can be generated using techniques like regression (logistic and linear), artificial neural network (ANN), and random forest (RF) and user-controlled spatial context. The statistical aids provided for parametric methods help selecting relevant drivers for each LULC class. The support for policy interventions such as goal-specific LULC class demand distribution, land conversion prioritization, and control over spatial mobility of land classes are possible by providing LULC demand, allocation-priority matrix, and class-inertia parameters. R and Python programming languages are used for development, considering portability to other open-source platforms. A case example presented here demonstrates the capabilities of the OpenLDM and the advantages of loose framework. It also illustrates estimation of optimum modeling parameters with improved quantitative and location agreement, selection of appropriate method(s) for generation of suitability maps for individual LULC classes under demand constraints and class-migration restrictions. The OpenLDM will be useful to researchers engaged in the domain of land system, ecosystem, and climate sciences.