Accurate　monitoring　of　urban　waterlogging　contributes　to　the　city＇s　normal　operation　and　the　safety　of　residents＇　daily　travel.　However,　due　to　feedback　delays　or　high　costs,　existing　methods　make　large-scale,　fine-grained　waterlogging　monitoring　impossible.　A　common　method　is　to　forecast　the　city＇s　global　waterlogging　status　using　its　partial　waterlogging　data.　This　method　has　two　challenges:　first,　existing　predictive　algorithms　are　either　driven　by　knowledge　or　data　alone;　and　second,　the　partial　waterlogging　data　is　not　collected　selectively,　resulting　in　poor　predictions.　To　overcome　the　aforementioned　challenges,　this　paper　proposes　a　framework　for　large-scale　and　fine-grained　spatiotemporal　waterlogging　monitoring　based　on　the　opportunistic　sensing　of　limited　bus　routes.　This　framework　follows　the　Sparse　Crowdsensing　and　mainly　comprises　a　pair　of　iterative　predictor　and　selector.　The　predictor　uses　the　collected　waterlogging　status　and　the　predicted　status　of　the　uncollected　area　to　train　the　graph　convolutional　neural　network.　It　combines　both　knowledge-driven　and　data-driven　approaches　and　can　be　used　to　forecast　waterlogging　status　in　all　regions　for　the　upcoming　term.　The　selector　consists　of　a　two-stage　selection　procedure　that　can　select　valuable　bus　routes　while　satisfying　budget　constraints.　The　experimental　results　on　real　waterlogging　and　bus　routes　in　Shenzhen　show　that　the　proposed　framework　could　easily　perform　urban　waterlogging　monitoring　with　low　cost,　high　accuracy,　wide　coverage,　and　fine　granularity.