We propose a novel method and system to prevent indiscriminate parking of dockless shared bicycles using location-based geo-fencing and image-based parking place identification. The geo-fencing is used to define the approximate regions for different types of bicycle parking regulations. The parking place identification uses a method based on deep Convolutional Neural Network (DCNN) to automatically identify designated bicycle parking places from photos captured by the cyclist using a mobile phone. Combining these two modalities, the parking of shared bicycles can be restricted in designated zones in various environments. Experiments are conducted using photos taken from the designated parking places with different parking indications at various locations. We evaluate the performance of the image-based parking place identification and use heatmaps to analyze potential features that are exploit by the DCNN models. The method achieves high performance on the testing dataset; and the features used for parking place identification are largely consistent with human perceptions.