Arbitrary-oriented　object　detection　(AOOD)　has　been　widely　applied　to　locate　and　classify　objects　with　diverse　orientations　in　remote　sensing　images.　However,　the　inconsistent　features　for　the　localization　and　classification　tasks　in　AOOD　models　may　lead　to　ambiguity　and　low-quality　object　predictions,　which　constrains　the　detection　performance.　In　this　article,　an　AOOD　method　called　task-wise　sampling　convolutions　(TS-Conv)　is　proposed.　TS-Conv　adaptively　samples　task-wise　features　from　respective　sensitive　regions　and　maps　these　features　together　in　alignment　to　guide　a　dynamic　label　assignment　for　better　predictions.　Specifically,　sampling　positions　of　the　localization　convolution　in　TS-Conv　are　supervised　by　the　oriented　bounding　box　(OBB)　prediction　associated　with　spatial　coordinates,　while　sampling　positions　and　convolutional　kernel　of　the　classification　convolution　are　designed　to　be　adaptively　adjusted　according　to　different　orientations　for　improving　the　orientation　robustness　of　features.　Furthermore,　a　dynamic　task-consistent-aware　label　assignment　(DTLA)　strategy　is　developed　to　select　optimal　candidate　positions　and　assign　labels　dynamically　according　to　ranked　task-aware　scores　obtained　from　TS-Conv.　Extensive　experiments　on　several　public　datasets　covering　multiple　scenes,　multimodal　images,　and　multiple　categories　of　objects　demonstrate　the　effectiveness,　scalability,　and　superior　performance　of　the　proposed　TS-Conv.